7 train Models By Tag

The following is a basic list of model types or relevant characteristics. There entires in these lists are arguable. For example: random forests theoretically use feature selection but effectively may not, support vector machines use L2 regularization etc.

Contents

7.0.1 Accepts Case Weights

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Adjacent Categories Probability Model for Ordinal Data

  method = 'vglmAdjCat'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Bagged CART

  method = 'treebag'

Type: Regression, Classification

No tuning parameters for this model

Required packages: ipred, plyr, e1071

A model-specific variable importance metric is available.

Bagged Flexible Discriminant Analysis

  method = 'bagFDA'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Bagged MARS

  method = 'bagEarth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged MARS using gCV Pruning

  method = 'bagEarthGCV'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bayesian Generalized Linear Model

  method = 'bayesglm'

Type: Regression, Classification

No tuning parameters for this model

Required packages: arm

Boosted Generalized Additive Model

  method = 'gamboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: mboost, plyr, import

Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Generalized Linear Model

  method = 'glmboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: plyr, mboost

A model-specific variable importance metric is available. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Tree

  method = 'blackboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: party, mboost, plyr

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

CART

  method = 'rpart'

Type: Regression, Classification

Tuning parameters:

  • cp (Complexity Parameter)

Required packages: rpart

A model-specific variable importance metric is available.

CART

  method = 'rpart1SE'

Type: Regression, Classification

No tuning parameters for this model

Required packages: rpart

A model-specific variable importance metric is available. Notes: This CART model replicates the same process used by the rpart function where the model complexity is determined using the one-standard error method. This procedure is replicated inside of the resampling done by train so that an external resampling estimate can be obtained.

CART

  method = 'rpart2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)

Required packages: rpart

A model-specific variable importance metric is available.

CART or Ordinal Responses

  method = 'rpartScore'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • split (Split Function)
  • prune (Pruning Measure)

Required packages: rpartScore, plyr

A model-specific variable importance metric is available.

CHi-squared Automated Interaction Detection

  method = 'chaid'

Type: Classification

Tuning parameters:

  • alpha2 (Merging Threshold)
  • alpha3 (Splitting former Merged Threshold)
  • alpha4 ( Splitting former Merged Threshold)

Required packages: CHAID

Conditional Inference Random Forest

  method = 'cforest'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: party

A model-specific variable importance metric is available.

Conditional Inference Tree

  method = 'ctree'

Type: Classification, Regression

Tuning parameters:

  • mincriterion (1 - P-Value Threshold)

Required packages: party

Conditional Inference Tree

  method = 'ctree2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)
  • mincriterion (1 - P-Value Threshold)

Required packages: party

Continuation Ratio Model for Ordinal Data

  method = 'vglmContRatio'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive CART

  method = 'rpartCost'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • Cost (Cost)

Required packages: rpart, plyr

Cumulative Probability Model for Ordinal Data

  method = 'vglmCumulative'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

eXtreme Gradient Boosting

  method = 'xgbTree'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • eta (Shrinkage)
  • gamma (Minimum Loss Reduction)
  • colsample_bytree (Subsample Ratio of Columns)
  • min_child_weight (Minimum Sum of Instance Weight)
  • subsample (Subsample Percentage)

Required packages: xgboost, plyr

A model-specific variable importance metric is available.

Flexible Discriminant Analysis

  method = 'fda'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Generalized Linear Model

  method = 'glm'

Type: Regression, Classification

No tuning parameters for this model

A model-specific variable importance metric is available.

Generalized Linear Model with Stepwise Feature Selection

  method = 'glmStepAIC'

Type: Regression, Classification

No tuning parameters for this model

Required packages: MASS

Linear Regression

  method = 'lm'

Type: Regression

Tuning parameters:

  • intercept (intercept)

A model-specific variable importance metric is available.

Linear Regression with Stepwise Selection

  method = 'lmStepAIC'

Type: Regression

No tuning parameters for this model

Required packages: MASS

Model Averaged Neural Network

  method = 'avNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)
  • bag (Bagging)

Required packages: nnet

Multivariate Adaptive Regression Spline

  method = 'earth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Multivariate Adaptive Regression Splines

  method = 'gcvEarth'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Negative Binomial Generalized Linear Model

  method = 'glm.nb'

Type: Regression

Tuning parameters:

  • link (Link Function)

A model-specific variable importance metric is available.

Neural Network

  method = 'nnet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Neural Networks with Feature Extraction

  method = 'pcaNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

Ordered Logistic or Probit Regression

  method = 'polr'

Type: Classification

Tuning parameters:

  • method (parameter)

Required packages: MASS

A model-specific variable importance metric is available.

Penalized Discriminant Analysis

  method = 'pda'

Type: Classification

Tuning parameters:

  • lambda (Shrinkage Penalty Coefficient)

Required packages: mda

Penalized Discriminant Analysis

  method = 'pda2'

Type: Classification

Tuning parameters:

  • df (Degrees of Freedom)

Required packages: mda

Penalized Multinomial Regression

  method = 'multinom'

Type: Classification

Tuning parameters:

  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Projection Pursuit Regression

  method = 'ppr'

Type: Regression

Tuning parameters:

  • nterms (# Terms)

Random Forest

  method = 'ranger'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • splitrule (Splitting Rule)

Required packages: e1071, ranger, dplyr

A model-specific variable importance metric is available.

Robust Linear Model

  method = 'rlm'

Type: Regression

Tuning parameters:

  • intercept (intercept)
  • psi (psi)

Required packages: MASS

Single C5.0 Ruleset

  method = 'C5.0Rules'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Single C5.0 Tree

  method = 'C5.0Tree'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Stochastic Gradient Boosting

  method = 'gbm'

Type: Regression, Classification

Tuning parameters:

  • n.trees (# Boosting Iterations)
  • interaction.depth (Max Tree Depth)
  • shrinkage (Shrinkage)
  • n.minobsinnode (Min. Terminal Node Size)

Required packages: gbm, plyr

A model-specific variable importance metric is available.

Tree Models from Genetic Algorithms

  method = 'evtree'

Type: Regression, Classification

Tuning parameters:

  • alpha (Complexity Parameter)

Required packages: evtree

7.0.2 Bagging

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Bagged AdaBoost

  method = 'AdaBag'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged CART

  method = 'treebag'

Type: Regression, Classification

No tuning parameters for this model

Required packages: ipred, plyr, e1071

A model-specific variable importance metric is available.

Bagged Flexible Discriminant Analysis

  method = 'bagFDA'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Bagged MARS

  method = 'bagEarth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged MARS using gCV Pruning

  method = 'bagEarthGCV'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged Model

  method = 'bag'

Type: Regression, Classification

Tuning parameters:

  • vars (#Randomly Selected Predictors)

Required packages: caret

Conditional Inference Random Forest

  method = 'cforest'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: party

A model-specific variable importance metric is available.

Ensembles of Generalized Linear Models

  method = 'randomGLM'

Type: Regression, Classification

Tuning parameters:

  • maxInteractionOrder (Interaction Order)

Required packages: randomGLM

Model Averaged Neural Network

  method = 'avNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)
  • bag (Bagging)

Required packages: nnet

Parallel Random Forest

  method = 'parRF'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: e1071, randomForest, foreach, import

A model-specific variable importance metric is available.

Quantile Random Forest

  method = 'qrf'

Type: Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: quantregForest

Quantile Regression Neural Network

  method = 'qrnn'

Type: Regression

Tuning parameters:

  • n.hidden (#Hidden Units)
  • penalty ( Weight Decay)
  • bag (Bagged Models?)

Required packages: qrnn

Random Ferns

  method = 'rFerns'

Type: Classification

Tuning parameters:

  • depth (Fern Depth)

Required packages: rFerns

Random Forest

  method = 'ranger'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • splitrule (Splitting Rule)

Required packages: e1071, ranger, dplyr

A model-specific variable importance metric is available.

Random Forest

  method = 'Rborist'

Type: Classification, Regression

Tuning parameters:

  • predFixed (#Randomly Selected Predictors)

Required packages: Rborist

A model-specific variable importance metric is available.

Random Forest

  method = 'rf'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: randomForest

A model-specific variable importance metric is available.

Random Forest by Randomization

  method = 'extraTrees'

Type: Regression, Classification

Tuning parameters:

  • mtry (# Randomly Selected Predictors)
  • numRandomCuts (# Random Cuts)

Required packages: extraTrees

Random Forest Rule-Based Model

  method = 'rfRules'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • maxdepth (Maximum Rule Depth)

Required packages: randomForest, inTrees, plyr

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRF'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)
  • coefImp (Importance Coefficient)

Required packages: randomForest, RRF

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRFglobal'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)

Required packages: RRF

A model-specific variable importance metric is available.

Weighted Subspace Random Forest

  method = 'wsrf'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: wsrf

7.0.3 Bayesian Model

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Bayesian Additive Regression Trees

  method = 'bartMachine'

Type: Classification, Regression

Tuning parameters:

  • num_trees (#Trees)
  • k (Prior Boundary)
  • alpha (Base Terminal Node Hyperparameter)
  • beta (Power Terminal Node Hyperparameter)
  • nu (Degrees of Freedom)

Required packages: bartMachine

A model-specific variable importance metric is available.

Bayesian Generalized Linear Model

  method = 'bayesglm'

Type: Regression, Classification

No tuning parameters for this model

Required packages: arm

Bayesian Regularized Neural Networks

  method = 'brnn'

Type: Regression

Tuning parameters:

  • neurons (# Neurons)

Required packages: brnn

Bayesian Ridge Regression

  method = 'bridge'

Type: Regression

No tuning parameters for this model

Required packages: monomvn

Bayesian Ridge Regression (Model Averaged)

  method = 'blassoAveraged'

Type: Regression

No tuning parameters for this model

Required packages: monomvn

Notes: This model makes predictions by averaging the predictions based on the posterior estimates of the regression coefficients. While it is possible that some of these posterior estimates are zero for non-informative predictors, the final predicted value may be a function of many (or even all) predictors.

Model Averaged Naive Bayes Classifier

  method = 'manb'

Type: Classification

Tuning parameters:

  • smooth (Smoothing Parameter)
  • prior (Prior Probability)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Naive Bayes

  method = 'naive_bayes'

Type: Classification

Tuning parameters:

  • fL (Laplace Correction)
  • usekernel (Distribution Type)
  • adjust (Bandwidth Adjustment)

Required packages: naivebayes

Naive Bayes

  method = 'nb'

Type: Classification

Tuning parameters:

  • fL (Laplace Correction)
  • usekernel (Distribution Type)
  • adjust (Bandwidth Adjustment)

Required packages: klaR

Naive Bayes Classifier

  method = 'nbDiscrete'

Type: Classification

Tuning parameters:

  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Naive Bayes Classifier with Attribute Weighting

  method = 'awnb'

Type: Classification

Tuning parameters:

  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Semi-Naive Structure Learner Wrapper

  method = 'nbSearch'

Type: Classification

Tuning parameters:

  • k (#Folds)
  • epsilon (Minimum Absolute Improvement)
  • smooth (Smoothing Parameter)
  • final_smooth (Final Smoothing Parameter)
  • direction (Search Direction)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Spike and Slab Regression

  method = 'spikeslab'

Type: Regression

Tuning parameters:

  • vars (Variables Retained)

Required packages: spikeslab, plyr

The Bayesian lasso

  method = 'blasso'

Type: Regression

Tuning parameters:

  • sparsity (Sparsity Threshold)

Required packages: monomvn

Notes: This model creates predictions using the mean of the posterior distributions but sets some parameters specifically to zero based on the tuning parameter sparsity. For example, when sparsity = .5, only coefficients where at least half the posterior estimates are nonzero are used.

Tree Augmented Naive Bayes Classifier

  method = 'tan'

Type: Classification

Tuning parameters:

  • score (Score Function)
  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Tree Augmented Naive Bayes Classifier Structure Learner Wrapper

  method = 'tanSearch'

Type: Classification

Tuning parameters:

  • k (#Folds)
  • epsilon (Minimum Absolute Improvement)
  • smooth (Smoothing Parameter)
  • final_smooth (Final Smoothing Parameter)
  • sp (Super-Parent)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Tree Augmented Naive Bayes Classifier with Attribute Weighting

  method = 'awtan'

Type: Classification

Tuning parameters:

  • score (Score Function)
  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Variational Bayesian Multinomial Probit Regression

  method = 'vbmpRadial'

Type: Classification

Tuning parameters:

  • estimateTheta (Theta Estimated)

Required packages: vbmp

7.0.4 Binary Predictors Only

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Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Binary Discriminant Analysis

  method = 'binda'

Type: Classification

Tuning parameters:

  • lambda.freqs (Shrinkage Intensity)

Required packages: binda

Logic Regression

  method = 'logreg'

Type: Regression, Classification

Tuning parameters:

  • treesize (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: LogicReg

7.0.5 Boosting

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AdaBoost Classification Trees

  method = 'adaboost'

Type: Classification

Tuning parameters:

  • nIter (#Trees)
  • method (Method)

Required packages: fastAdaboost

AdaBoost.M1

  method = 'AdaBoost.M1'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)
  • coeflearn (Coefficient Type)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged AdaBoost

  method = 'AdaBag'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Boosted Classification Trees

  method = 'ada'

Type: Classification

Tuning parameters:

  • iter (#Trees)
  • maxdepth (Max Tree Depth)
  • nu (Learning Rate)

Required packages: ada, plyr

Boosted Generalized Additive Model

  method = 'gamboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: mboost, plyr, import

Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Generalized Linear Model

  method = 'glmboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: plyr, mboost

A model-specific variable importance metric is available. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Linear Model

  method = 'BstLm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

Boosted Logistic Regression

  method = 'LogitBoost'

Type: Classification

Tuning parameters:

  • nIter (# Boosting Iterations)

Required packages: caTools

Boosted Smoothing Spline

  method = 'bstSm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

Boosted Tree

  method = 'blackboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: party, mboost, plyr

Boosted Tree

  method = 'bstTree'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • maxdepth (Max Tree Depth)
  • nu (Shrinkage)

Required packages: bst, plyr

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

eXtreme Gradient Boosting

  method = 'xgbLinear'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • lambda (L2 Regularization)
  • alpha (L1 Regularization)
  • eta (Learning Rate)

Required packages: xgboost

A model-specific variable importance metric is available.

eXtreme Gradient Boosting

  method = 'xgbTree'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • eta (Shrinkage)
  • gamma (Minimum Loss Reduction)
  • colsample_bytree (Subsample Ratio of Columns)
  • min_child_weight (Minimum Sum of Instance Weight)
  • subsample (Subsample Percentage)

Required packages: xgboost, plyr

A model-specific variable importance metric is available.

Gradient Boosting Machines

  method = 'gbm_h2o'

Type: Regression, Classification

Tuning parameters:

  • ntrees (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • min_rows (Min. Terminal Node Size)
  • learn_rate (Shrinkage)
  • col_sample_rate (#Randomly Selected Predictors)

Required packages: h2o

A model-specific variable importance metric is available.

Stochastic Gradient Boosting

  method = 'gbm'

Type: Regression, Classification

Tuning parameters:

  • n.trees (# Boosting Iterations)
  • interaction.depth (Max Tree Depth)
  • shrinkage (Shrinkage)
  • n.minobsinnode (Min. Terminal Node Size)

Required packages: gbm, plyr

A model-specific variable importance metric is available.

7.0.6 Categorical Predictors Only

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Model Averaged Naive Bayes Classifier

  method = 'manb'

Type: Classification

Tuning parameters:

  • smooth (Smoothing Parameter)
  • prior (Prior Probability)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Naive Bayes Classifier

  method = 'nbDiscrete'

Type: Classification

Tuning parameters:

  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Naive Bayes Classifier with Attribute Weighting

  method = 'awnb'

Type: Classification

Tuning parameters:

  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Semi-Naive Structure Learner Wrapper

  method = 'nbSearch'

Type: Classification

Tuning parameters:

  • k (#Folds)
  • epsilon (Minimum Absolute Improvement)
  • smooth (Smoothing Parameter)
  • final_smooth (Final Smoothing Parameter)
  • direction (Search Direction)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Tree Augmented Naive Bayes Classifier

  method = 'tan'

Type: Classification

Tuning parameters:

  • score (Score Function)
  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Tree Augmented Naive Bayes Classifier Structure Learner Wrapper

  method = 'tanSearch'

Type: Classification

Tuning parameters:

  • k (#Folds)
  • epsilon (Minimum Absolute Improvement)
  • smooth (Smoothing Parameter)
  • final_smooth (Final Smoothing Parameter)
  • sp (Super-Parent)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

Tree Augmented Naive Bayes Classifier with Attribute Weighting

  method = 'awtan'

Type: Classification

Tuning parameters:

  • score (Score Function)
  • smooth (Smoothing Parameter)

Required packages: bnclassify

Notes: Not on CRAN but can be installed from GitHub at bmihaljevic/bnclassify.

7.0.7 Cost Sensitive Learning

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Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive CART

  method = 'rpartCost'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • Cost (Cost)

Required packages: rpart, plyr

L2 Regularized Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights2'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)
  • weight (Class Weight)

Required packages: LiblineaR

Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • weight (Class Weight)

Required packages: e1071

Multilayer Perceptron Network with Dropout

  method = 'mlpKerasDropoutCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • dropout (Dropout Rate)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Multilayer Perceptron Network with Weight Decay

  method = 'mlpKerasDecayCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • lambda (L2 Regularization)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Support Vector Machines with Class Weights

  method = 'svmRadialWeights'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)
  • Weight (Weight)

Required packages: kernlab

7.0.8 Discriminant Analysis

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Adaptive Mixture Discriminant Analysis

  method = 'amdai'

Type: Classification

Tuning parameters:

  • model (Model Type)

Required packages: adaptDA

Binary Discriminant Analysis

  method = 'binda'

Type: Classification

Tuning parameters:

  • lambda.freqs (Shrinkage Intensity)

Required packages: binda

Diagonal Discriminant Analysis

  method = 'dda'

Type: Classification

Tuning parameters:

  • model (Model)
  • shrinkage (Shrinkage Type)

Required packages: sparsediscrim

Distance Weighted Discrimination with Polynomial Kernel

  method = 'dwdPoly'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kerndwd

Distance Weighted Discrimination with Radial Basis Function Kernel

  method = 'dwdRadial'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • sigma (Sigma)

Required packages: kernlab, kerndwd

Factor-Based Linear Discriminant Analysis

  method = 'RFlda'

Type: Classification

Tuning parameters:

  • q (# Factors)

Required packages: HiDimDA

Heteroscedastic Discriminant Analysis

  method = 'hda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • newdim (Dimension of the Discriminative Subspace)

Required packages: hda

High Dimensional Discriminant Analysis

  method = 'hdda'

Type: Classification

Tuning parameters:

  • threshold (Threshold)
  • model (Model Type)

Required packages: HDclassif

High-Dimensional Regularized Discriminant Analysis

  method = 'hdrda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • shrinkage_type (Shrinkage Type)

Required packages: sparsediscrim

Linear Discriminant Analysis

  method = 'lda'

Type: Classification

No tuning parameters for this model

Required packages: MASS

Linear Discriminant Analysis

  method = 'lda2'

Type: Classification

Tuning parameters:

  • dimen (#Discriminant Functions)

Required packages: MASS

Linear Discriminant Analysis with Stepwise Feature Selection

  method = 'stepLDA'

Type: Classification

Tuning parameters:

  • maxvar (Maximum #Variables)
  • direction (Search Direction)

Required packages: klaR, MASS

Linear Distance Weighted Discrimination

  method = 'dwdLinear'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)

Required packages: kerndwd

Localized Linear Discriminant Analysis

  method = 'loclda'

Type: Classification

Tuning parameters:

  • k (#Nearest Neighbors)

Required packages: klaR

Maximum Uncertainty Linear Discriminant Analysis

  method = 'Mlda'

Type: Classification

No tuning parameters for this model

Required packages: HiDimDA

Mixture Discriminant Analysis

  method = 'mda'

Type: Classification

Tuning parameters:

  • subclasses (#Subclasses Per Class)

Required packages: mda

Penalized Discriminant Analysis

  method = 'pda'

Type: Classification

Tuning parameters:

  • lambda (Shrinkage Penalty Coefficient)

Required packages: mda

Penalized Discriminant Analysis

  method = 'pda2'

Type: Classification

Tuning parameters:

  • df (Degrees of Freedom)

Required packages: mda

Penalized Linear Discriminant Analysis

  method = 'PenalizedLDA'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • K (#Discriminant Functions)

Required packages: penalizedLDA, plyr

Quadratic Discriminant Analysis

  method = 'qda'

Type: Classification

No tuning parameters for this model

Required packages: MASS

Quadratic Discriminant Analysis with Stepwise Feature Selection

  method = 'stepQDA'

Type: Classification

Tuning parameters:

  • maxvar (Maximum #Variables)
  • direction (Search Direction)

Required packages: klaR, MASS

Regularized Discriminant Analysis

  method = 'rda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)

Required packages: klaR

Regularized Linear Discriminant Analysis

  method = 'rlda'

Type: Classification

Tuning parameters:

  • estimator (Regularization Method)

Required packages: sparsediscrim

Robust Linear Discriminant Analysis

  method = 'Linda'

Type: Classification

No tuning parameters for this model

Required packages: rrcov

Robust Mixture Discriminant Analysis

  method = 'rmda'

Type: Classification

Tuning parameters:

  • K (#Subclasses Per Class)
  • model (Model)

Required packages: robustDA

Robust Quadratic Discriminant Analysis

  method = 'QdaCov'

Type: Classification

No tuning parameters for this model

Required packages: rrcov

Robust Regularized Linear Discriminant Analysis

  method = 'rrlda'

Type: Classification

Tuning parameters:

  • lambda (Penalty Parameter)
  • hp (Robustness Parameter)
  • penalty (Penalty Type)

Required packages: rrlda

Shrinkage Discriminant Analysis

  method = 'sda'

Type: Classification

Tuning parameters:

  • diagonal (Diagonalize)
  • lambda (shrinkage)

Required packages: sda

Sparse Linear Discriminant Analysis

  method = 'sparseLDA'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)

Required packages: sparseLDA

Sparse Mixture Discriminant Analysis

  method = 'smda'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)
  • R (# Subclasses)

Required packages: sparseLDA

Stabilized Linear Discriminant Analysis

  method = 'slda'

Type: Classification

No tuning parameters for this model

Required packages: ipred

7.0.9 Distance Weighted Discrimination

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Distance Weighted Discrimination with Polynomial Kernel

  method = 'dwdPoly'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kerndwd

Distance Weighted Discrimination with Radial Basis Function Kernel

  method = 'dwdRadial'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • sigma (Sigma)

Required packages: kernlab, kerndwd

Linear Distance Weighted Discrimination

  method = 'dwdLinear'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)

Required packages: kerndwd

Sparse Distance Weighted Discrimination

  method = 'sdwd'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: sdwd

A model-specific variable importance metric is available.

7.0.10 Ensemble Model

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AdaBoost Classification Trees

  method = 'adaboost'

Type: Classification

Tuning parameters:

  • nIter (#Trees)
  • method (Method)

Required packages: fastAdaboost

AdaBoost.M1

  method = 'AdaBoost.M1'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)
  • coeflearn (Coefficient Type)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged AdaBoost

  method = 'AdaBag'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged CART

  method = 'treebag'

Type: Regression, Classification

No tuning parameters for this model

Required packages: ipred, plyr, e1071

A model-specific variable importance metric is available.

Bagged Flexible Discriminant Analysis

  method = 'bagFDA'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Bagged MARS

  method = 'bagEarth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged MARS using gCV Pruning

  method = 'bagEarthGCV'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged Model

  method = 'bag'

Type: Regression, Classification

Tuning parameters:

  • vars (#Randomly Selected Predictors)

Required packages: caret

Boosted Classification Trees

  method = 'ada'

Type: Classification

Tuning parameters:

  • iter (#Trees)
  • maxdepth (Max Tree Depth)
  • nu (Learning Rate)

Required packages: ada, plyr

Boosted Generalized Additive Model

  method = 'gamboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: mboost, plyr, import

Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Generalized Linear Model

  method = 'glmboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: plyr, mboost

A model-specific variable importance metric is available. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Linear Model

  method = 'BstLm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

Boosted Logistic Regression

  method = 'LogitBoost'

Type: Classification

Tuning parameters:

  • nIter (# Boosting Iterations)

Required packages: caTools

Boosted Smoothing Spline

  method = 'bstSm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

Boosted Tree

  method = 'blackboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: party, mboost, plyr

Boosted Tree

  method = 'bstTree'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • maxdepth (Max Tree Depth)
  • nu (Shrinkage)

Required packages: bst, plyr

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Conditional Inference Random Forest

  method = 'cforest'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: party

A model-specific variable importance metric is available.

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

Ensembles of Generalized Linear Models

  method = 'randomGLM'

Type: Regression, Classification

Tuning parameters:

  • maxInteractionOrder (Interaction Order)

Required packages: randomGLM

eXtreme Gradient Boosting

  method = 'xgbLinear'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • lambda (L2 Regularization)
  • alpha (L1 Regularization)
  • eta (Learning Rate)

Required packages: xgboost

A model-specific variable importance metric is available.

eXtreme Gradient Boosting

  method = 'xgbTree'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • eta (Shrinkage)
  • gamma (Minimum Loss Reduction)
  • colsample_bytree (Subsample Ratio of Columns)
  • min_child_weight (Minimum Sum of Instance Weight)
  • subsample (Subsample Percentage)

Required packages: xgboost, plyr

A model-specific variable importance metric is available.

Gradient Boosting Machines

  method = 'gbm_h2o'

Type: Regression, Classification

Tuning parameters:

  • ntrees (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • min_rows (Min. Terminal Node Size)
  • learn_rate (Shrinkage)
  • col_sample_rate (#Randomly Selected Predictors)

Required packages: h2o

A model-specific variable importance metric is available.

Model Averaged Neural Network

  method = 'avNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)
  • bag (Bagging)

Required packages: nnet

Oblique Random Forest

  method = 'ORFlog'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFpls'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFsvm'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Parallel Random Forest

  method = 'parRF'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: e1071, randomForest, foreach, import

A model-specific variable importance metric is available.

Quantile Random Forest

  method = 'qrf'

Type: Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: quantregForest

Quantile Regression Neural Network

  method = 'qrnn'

Type: Regression

Tuning parameters:

  • n.hidden (#Hidden Units)
  • penalty ( Weight Decay)
  • bag (Bagged Models?)

Required packages: qrnn

Random Ferns

  method = 'rFerns'

Type: Classification

Tuning parameters:

  • depth (Fern Depth)

Required packages: rFerns

Random Forest

  method = 'ranger'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • splitrule (Splitting Rule)

Required packages: e1071, ranger, dplyr

A model-specific variable importance metric is available.

Random Forest

  method = 'Rborist'

Type: Classification, Regression

Tuning parameters:

  • predFixed (#Randomly Selected Predictors)

Required packages: Rborist

A model-specific variable importance metric is available.

Random Forest

  method = 'rf'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: randomForest

A model-specific variable importance metric is available.

Random Forest by Randomization

  method = 'extraTrees'

Type: Regression, Classification

Tuning parameters:

  • mtry (# Randomly Selected Predictors)
  • numRandomCuts (# Random Cuts)

Required packages: extraTrees

Random Forest Rule-Based Model

  method = 'rfRules'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • maxdepth (Maximum Rule Depth)

Required packages: randomForest, inTrees, plyr

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRF'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)
  • coefImp (Importance Coefficient)

Required packages: randomForest, RRF

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRFglobal'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)

Required packages: RRF

A model-specific variable importance metric is available.

Rotation Forest

  method = 'rotationForest'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)

Required packages: rotationForest

A model-specific variable importance metric is available.

Rotation Forest

  method = 'rotationForestCp'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)
  • cp (Complexity Parameter)

Required packages: rpart, plyr, rotationForest

A model-specific variable importance metric is available.

Stochastic Gradient Boosting

  method = 'gbm'

Type: Regression, Classification

Tuning parameters:

  • n.trees (# Boosting Iterations)
  • interaction.depth (Max Tree Depth)
  • shrinkage (Shrinkage)
  • n.minobsinnode (Min. Terminal Node Size)

Required packages: gbm, plyr

A model-specific variable importance metric is available.

Tree-Based Ensembles

  method = 'nodeHarvest'

Type: Regression, Classification

Tuning parameters:

  • maxinter (Maximum Interaction Depth)
  • mode (Prediction Mode)

Required packages: nodeHarvest

Weighted Subspace Random Forest

  method = 'wsrf'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: wsrf

7.0.11 Feature Extraction

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Independent Component Regression

  method = 'icr'

Type: Regression

Tuning parameters:

  • n.comp (#Components)

Required packages: fastICA

Neural Networks with Feature Extraction

  method = 'pcaNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

Partial Least Squares

  method = 'kernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'pls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'simpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'widekernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Principal Component Analysis

  method = 'pcr'

Type: Regression

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

Projection Pursuit Regression

  method = 'ppr'

Type: Regression

Tuning parameters:

  • nterms (# Terms)

Sparse Partial Least Squares

  method = 'spls'

Type: Regression, Classification

Tuning parameters:

  • K (#Components)
  • eta (Threshold)
  • kappa (Kappa)

Required packages: spls

Supervised Principal Component Analysis

  method = 'superpc'

Type: Regression

Tuning parameters:

  • threshold (Threshold)
  • n.components (#Components)

Required packages: superpc

7.0.12 Feature Selection Wrapper

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Generalized Linear Model with Stepwise Feature Selection

  method = 'glmStepAIC'

Type: Regression, Classification

No tuning parameters for this model

Required packages: MASS

Linear Discriminant Analysis with Stepwise Feature Selection

  method = 'stepLDA'

Type: Classification

Tuning parameters:

  • maxvar (Maximum #Variables)
  • direction (Search Direction)

Required packages: klaR, MASS

Linear Regression with Backwards Selection

  method = 'leapBackward'

Type: Regression

Tuning parameters:

  • nvmax (Maximum Number of Predictors)

Required packages: leaps

Linear Regression with Forward Selection

  method = 'leapForward'

Type: Regression

Tuning parameters:

  • nvmax (Maximum Number of Predictors)

Required packages: leaps

Linear Regression with Stepwise Selection

  method = 'leapSeq'

Type: Regression

Tuning parameters:

  • nvmax (Maximum Number of Predictors)

Required packages: leaps

Linear Regression with Stepwise Selection

  method = 'lmStepAIC'

Type: Regression

No tuning parameters for this model

Required packages: MASS

Quadratic Discriminant Analysis with Stepwise Feature Selection

  method = 'stepQDA'

Type: Classification

Tuning parameters:

  • maxvar (Maximum #Variables)
  • direction (Search Direction)

Required packages: klaR, MASS

Ridge Regression with Variable Selection

  method = 'foba'

Type: Regression

Tuning parameters:

  • k (#Variables Retained)
  • lambda (L2 Penalty)

Required packages: foba

7.0.13 Gaussian Process

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Gaussian Process

  method = 'gaussprLinear'

Type: Regression, Classification

No tuning parameters for this model

Required packages: kernlab

Gaussian Process with Polynomial Kernel

  method = 'gaussprPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kernlab

Gaussian Process with Radial Basis Function Kernel

  method = 'gaussprRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

Variational Bayesian Multinomial Probit Regression

  method = 'vbmpRadial'

Type: Classification

Tuning parameters:

  • estimateTheta (Theta Estimated)

Required packages: vbmp

7.0.14 Generalized Additive Model

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Boosted Generalized Additive Model

  method = 'gamboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: mboost, plyr, import

Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Generalized Additive Model using LOESS

  method = 'gamLoess'

Type: Regression, Classification

Tuning parameters:

  • span (Span)
  • degree (Degree)

Required packages: gam

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Additive Model using Splines

  method = 'bam'

Type: Regression, Classification

Tuning parameters:

  • select (Feature Selection)
  • method (Method)

Required packages: mgcv

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Additive Model using Splines

  method = 'gam'

Type: Regression, Classification

Tuning parameters:

  • select (Feature Selection)
  • method (Method)

Required packages: mgcv

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Additive Model using Splines

  method = 'gamSpline'

Type: Regression, Classification

Tuning parameters:

  • df (Degrees of Freedom)

Required packages: gam

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

7.0.15 Generalized Linear Model

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Bayesian Generalized Linear Model

  method = 'bayesglm'

Type: Regression, Classification

No tuning parameters for this model

Required packages: arm

Boosted Generalized Linear Model

  method = 'glmboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: plyr, mboost

A model-specific variable importance metric is available. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Ensembles of Generalized Linear Models

  method = 'randomGLM'

Type: Regression, Classification

Tuning parameters:

  • maxInteractionOrder (Interaction Order)

Required packages: randomGLM

Generalized Additive Model using LOESS

  method = 'gamLoess'

Type: Regression, Classification

Tuning parameters:

  • span (Span)
  • degree (Degree)

Required packages: gam

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Additive Model using Splines

  method = 'bam'

Type: Regression, Classification

Tuning parameters:

  • select (Feature Selection)
  • method (Method)

Required packages: mgcv

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Additive Model using Splines

  method = 'gam'

Type: Regression, Classification

Tuning parameters:

  • select (Feature Selection)
  • method (Method)

Required packages: mgcv

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Additive Model using Splines

  method = 'gamSpline'

Type: Regression, Classification

Tuning parameters:

  • df (Degrees of Freedom)

Required packages: gam

A model-specific variable importance metric is available. Notes: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor. For example, if a predictor only has four unique values, most basis expansion method will fail because there are not enough granularity in the data. By default, a predictor must have at least 10 unique values to be used in a nonlinear basis expansion.

Generalized Linear Model

  method = 'glm'

Type: Regression, Classification

No tuning parameters for this model

A model-specific variable importance metric is available.

Generalized Linear Model with Stepwise Feature Selection

  method = 'glmStepAIC'

Type: Regression, Classification

No tuning parameters for this model

Required packages: MASS

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

glmnet

  method = 'glmnet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: glmnet, Matrix

A model-specific variable importance metric is available.

Multi-Step Adaptive MCP-Net

  method = 'msaenet'

Type: Regression, Classification

Tuning parameters:

  • alphas (Alpha)
  • nsteps (#Adaptive Estimation Steps)
  • scale (Adaptive Weight Scaling Factor)

Required packages: msaenet

A model-specific variable importance metric is available.

Negative Binomial Generalized Linear Model

  method = 'glm.nb'

Type: Regression

Tuning parameters:

  • link (Link Function)

A model-specific variable importance metric is available.

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

7.0.16 Handle Missing Predictor Data

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AdaBoost.M1

  method = 'AdaBoost.M1'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)
  • coeflearn (Coefficient Type)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged AdaBoost

  method = 'AdaBag'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Boosted Classification Trees

  method = 'ada'

Type: Classification

Tuning parameters:

  • iter (#Trees)
  • maxdepth (Max Tree Depth)
  • nu (Learning Rate)

Required packages: ada, plyr

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

CART

  method = 'rpart'

Type: Regression, Classification

Tuning parameters:

  • cp (Complexity Parameter)

Required packages: rpart

A model-specific variable importance metric is available.

CART

  method = 'rpart1SE'

Type: Regression, Classification

No tuning parameters for this model

Required packages: rpart

A model-specific variable importance metric is available. Notes: This CART model replicates the same process used by the rpart function where the model complexity is determined using the one-standard error method. This procedure is replicated inside of the resampling done by train so that an external resampling estimate can be obtained.

CART

  method = 'rpart2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)

Required packages: rpart

A model-specific variable importance metric is available.

CART or Ordinal Responses

  method = 'rpartScore'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • split (Split Function)
  • prune (Pruning Measure)

Required packages: rpartScore, plyr

A model-specific variable importance metric is available.

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive CART

  method = 'rpartCost'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • Cost (Cost)

Required packages: rpart, plyr

Single C5.0 Ruleset

  method = 'C5.0Rules'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Single C5.0 Tree

  method = 'C5.0Tree'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

7.0.17 Implicit Feature Selection

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AdaBoost Classification Trees

  method = 'adaboost'

Type: Classification

Tuning parameters:

  • nIter (#Trees)
  • method (Method)

Required packages: fastAdaboost

AdaBoost.M1

  method = 'AdaBoost.M1'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)
  • coeflearn (Coefficient Type)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged AdaBoost

  method = 'AdaBag'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged Flexible Discriminant Analysis

  method = 'bagFDA'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Bagged MARS

  method = 'bagEarth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged MARS using gCV Pruning

  method = 'bagEarthGCV'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bayesian Additive Regression Trees

  method = 'bartMachine'

Type: Classification, Regression

Tuning parameters:

  • num_trees (#Trees)
  • k (Prior Boundary)
  • alpha (Base Terminal Node Hyperparameter)
  • beta (Power Terminal Node Hyperparameter)
  • nu (Degrees of Freedom)

Required packages: bartMachine

A model-specific variable importance metric is available.

Boosted Classification Trees

  method = 'ada'

Type: Classification

Tuning parameters:

  • iter (#Trees)
  • maxdepth (Max Tree Depth)
  • nu (Learning Rate)

Required packages: ada, plyr

Boosted Generalized Additive Model

  method = 'gamboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: mboost, plyr, import

Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Linear Model

  method = 'BstLm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

Boosted Logistic Regression

  method = 'LogitBoost'

Type: Classification

Tuning parameters:

  • nIter (# Boosting Iterations)

Required packages: caTools

Boosted Smoothing Spline

  method = 'bstSm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

C4.5-like Trees

  method = 'J48'

Type: Classification

Tuning parameters:

  • C (Confidence Threshold)
  • M (Minimum Instances Per Leaf)

Required packages: RWeka

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

CART

  method = 'rpart'

Type: Regression, Classification

Tuning parameters:

  • cp (Complexity Parameter)

Required packages: rpart

A model-specific variable importance metric is available.

CART

  method = 'rpart1SE'

Type: Regression, Classification

No tuning parameters for this model

Required packages: rpart

A model-specific variable importance metric is available. Notes: This CART model replicates the same process used by the rpart function where the model complexity is determined using the one-standard error method. This procedure is replicated inside of the resampling done by train so that an external resampling estimate can be obtained.

CART

  method = 'rpart2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)

Required packages: rpart

A model-specific variable importance metric is available.

CART or Ordinal Responses

  method = 'rpartScore'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • split (Split Function)
  • prune (Pruning Measure)

Required packages: rpartScore, plyr

A model-specific variable importance metric is available.

CHi-squared Automated Interaction Detection

  method = 'chaid'

Type: Classification

Tuning parameters:

  • alpha2 (Merging Threshold)
  • alpha3 (Splitting former Merged Threshold)
  • alpha4 ( Splitting former Merged Threshold)

Required packages: CHAID

Conditional Inference Random Forest

  method = 'cforest'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: party

A model-specific variable importance metric is available.

Conditional Inference Tree

  method = 'ctree'

Type: Classification, Regression

Tuning parameters:

  • mincriterion (1 - P-Value Threshold)

Required packages: party

Conditional Inference Tree

  method = 'ctree2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)
  • mincriterion (1 - P-Value Threshold)

Required packages: party

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive CART

  method = 'rpartCost'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • Cost (Cost)

Required packages: rpart, plyr

Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

Elasticnet

  method = 'enet'

Type: Regression

Tuning parameters:

  • fraction (Fraction of Full Solution)
  • lambda (Weight Decay)

Required packages: elasticnet

eXtreme Gradient Boosting

  method = 'xgbLinear'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • lambda (L2 Regularization)
  • alpha (L1 Regularization)
  • eta (Learning Rate)

Required packages: xgboost

A model-specific variable importance metric is available.

eXtreme Gradient Boosting

  method = 'xgbTree'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • eta (Shrinkage)
  • gamma (Minimum Loss Reduction)
  • colsample_bytree (Subsample Ratio of Columns)
  • min_child_weight (Minimum Sum of Instance Weight)
  • subsample (Subsample Percentage)

Required packages: xgboost, plyr

A model-specific variable importance metric is available.

Flexible Discriminant Analysis

  method = 'fda'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Generalized Linear Model with Stepwise Feature Selection

  method = 'glmStepAIC'

Type: Regression, Classification

No tuning parameters for this model

Required packages: MASS

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

glmnet

  method = 'glmnet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: glmnet, Matrix

A model-specific variable importance metric is available.

Gradient Boosting Machines

  method = 'gbm_h2o'

Type: Regression, Classification

Tuning parameters:

  • ntrees (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • min_rows (Min. Terminal Node Size)
  • learn_rate (Shrinkage)
  • col_sample_rate (#Randomly Selected Predictors)

Required packages: h2o

A model-specific variable importance metric is available.

Least Angle Regression

  method = 'lars'

Type: Regression

Tuning parameters:

  • fraction (Fraction)

Required packages: lars

Least Angle Regression

  method = 'lars2'

Type: Regression

Tuning parameters:

  • step (#Steps)

Required packages: lars

Logistic Model Trees

  method = 'LMT'

Type: Classification

Tuning parameters:

  • iter (# Iteratons)

Required packages: RWeka

Model Rules

  method = 'M5Rules'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)

Required packages: RWeka

Model Tree

  method = 'M5'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)
  • rules (Rules)

Required packages: RWeka

Multi-Step Adaptive MCP-Net

  method = 'msaenet'

Type: Regression, Classification

Tuning parameters:

  • alphas (Alpha)
  • nsteps (#Adaptive Estimation Steps)
  • scale (Adaptive Weight Scaling Factor)

Required packages: msaenet

A model-specific variable importance metric is available.

Multivariate Adaptive Regression Spline

  method = 'earth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Multivariate Adaptive Regression Splines

  method = 'gcvEarth'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Nearest Shrunken Centroids

  method = 'pam'

Type: Classification

Tuning parameters:

  • threshold (Shrinkage Threshold)

Required packages: pamr

A model-specific variable importance metric is available.

Non-Convex Penalized Quantile Regression

  method = 'rqnc'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)
  • penalty (Penalty Type)

Required packages: rqPen

Oblique Random Forest

  method = 'ORFlog'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFpls'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFsvm'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Parallel Random Forest

  method = 'parRF'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: e1071, randomForest, foreach, import

A model-specific variable importance metric is available.

Penalized Linear Discriminant Analysis

  method = 'PenalizedLDA'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • K (#Discriminant Functions)

Required packages: penalizedLDA, plyr

Penalized Linear Regression

  method = 'penalized'

Type: Regression

Tuning parameters:

  • lambda1 (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: penalized

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

Quantile Random Forest

  method = 'qrf'

Type: Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: quantregForest

Quantile Regression with LASSO penalty

  method = 'rqlasso'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)

Required packages: rqPen

Random Ferns

  method = 'rFerns'

Type: Classification

Tuning parameters:

  • depth (Fern Depth)

Required packages: rFerns

Random Forest

  method = 'ranger'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • splitrule (Splitting Rule)

Required packages: e1071, ranger, dplyr

A model-specific variable importance metric is available.

Random Forest

  method = 'Rborist'

Type: Classification, Regression

Tuning parameters:

  • predFixed (#Randomly Selected Predictors)

Required packages: Rborist

A model-specific variable importance metric is available.

Random Forest

  method = 'rf'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: randomForest

A model-specific variable importance metric is available.

Random Forest by Randomization

  method = 'extraTrees'

Type: Regression, Classification

Tuning parameters:

  • mtry (# Randomly Selected Predictors)
  • numRandomCuts (# Random Cuts)

Required packages: extraTrees

Random Forest Rule-Based Model

  method = 'rfRules'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • maxdepth (Maximum Rule Depth)

Required packages: randomForest, inTrees, plyr

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRF'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)
  • coefImp (Importance Coefficient)

Required packages: randomForest, RRF

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRFglobal'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)

Required packages: RRF

A model-specific variable importance metric is available.

Relaxed Lasso

  method = 'relaxo'

Type: Regression

Tuning parameters:

  • lambda (Penalty Parameter)
  • phi (Relaxation Parameter)

Required packages: relaxo, plyr

Rotation Forest

  method = 'rotationForest'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)

Required packages: rotationForest

A model-specific variable importance metric is available.

Rotation Forest

  method = 'rotationForestCp'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)
  • cp (Complexity Parameter)

Required packages: rpart, plyr, rotationForest

A model-specific variable importance metric is available.

Rule-Based Classifier

  method = 'JRip'

Type: Classification

Tuning parameters:

  • NumOpt (# Optimizations)
  • NumFolds (# Folds)
  • MinWeights (Min Weights)

Required packages: RWeka

A model-specific variable importance metric is available.

Rule-Based Classifier

  method = 'PART'

Type: Classification

Tuning parameters:

  • threshold (Confidence Threshold)
  • pruned (Pruning)

Required packages: RWeka

A model-specific variable importance metric is available.

Single C5.0 Ruleset

  method = 'C5.0Rules'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Single C5.0 Tree

  method = 'C5.0Tree'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Single Rule Classification

  method = 'OneR'

Type: Classification

No tuning parameters for this model

Required packages: RWeka

Sparse Distance Weighted Discrimination

  method = 'sdwd'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: sdwd

A model-specific variable importance metric is available.

Sparse Linear Discriminant Analysis

  method = 'sparseLDA'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)

Required packages: sparseLDA

Sparse Mixture Discriminant Analysis

  method = 'smda'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)
  • R (# Subclasses)

Required packages: sparseLDA

Spike and Slab Regression

  method = 'spikeslab'

Type: Regression

Tuning parameters:

  • vars (Variables Retained)

Required packages: spikeslab, plyr

Stochastic Gradient Boosting

  method = 'gbm'

Type: Regression, Classification

Tuning parameters:

  • n.trees (# Boosting Iterations)
  • interaction.depth (Max Tree Depth)
  • shrinkage (Shrinkage)
  • n.minobsinnode (Min. Terminal Node Size)

Required packages: gbm, plyr

A model-specific variable importance metric is available.

The Bayesian lasso

  method = 'blasso'

Type: Regression

Tuning parameters:

  • sparsity (Sparsity Threshold)

Required packages: monomvn

Notes: This model creates predictions using the mean of the posterior distributions but sets some parameters specifically to zero based on the tuning parameter sparsity. For example, when sparsity = .5, only coefficients where at least half the posterior estimates are nonzero are used.

The lasso

  method = 'lasso'

Type: Regression

Tuning parameters:

  • fraction (Fraction of Full Solution)

Required packages: elasticnet

Tree-Based Ensembles

  method = 'nodeHarvest'

Type: Regression, Classification

Tuning parameters:

  • maxinter (Maximum Interaction Depth)
  • mode (Prediction Mode)

Required packages: nodeHarvest

Tree Models from Genetic Algorithms

  method = 'evtree'

Type: Regression, Classification

Tuning parameters:

  • alpha (Complexity Parameter)

Required packages: evtree

Weighted Subspace Random Forest

  method = 'wsrf'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: wsrf

7.0.18 Kernel Method

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Distance Weighted Discrimination with Polynomial Kernel

  method = 'dwdPoly'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kerndwd

Distance Weighted Discrimination with Radial Basis Function Kernel

  method = 'dwdRadial'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • sigma (Sigma)

Required packages: kernlab, kerndwd

Gaussian Process

  method = 'gaussprLinear'

Type: Regression, Classification

No tuning parameters for this model

Required packages: kernlab

Gaussian Process with Polynomial Kernel

  method = 'gaussprPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kernlab

Gaussian Process with Radial Basis Function Kernel

  method = 'gaussprRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

L2 Regularized Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights2'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)
  • weight (Class Weight)

Required packages: LiblineaR

L2 Regularized Support Vector Machine (dual) with Linear Kernel

  method = 'svmLinear3'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)

Required packages: LiblineaR

Least Squares Support Vector Machine

  method = 'lssvmLinear'

Type: Classification

Tuning parameters:

  • tau (Regularization Parameter)

Required packages: kernlab

Least Squares Support Vector Machine with Polynomial Kernel

  method = 'lssvmPoly'

Type: Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • tau (Regularization Parameter)

Required packages: kernlab

Least Squares Support Vector Machine with Radial Basis Function Kernel

  method = 'lssvmRadial'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • tau (Regularization Parameter)

Required packages: kernlab

Linear Distance Weighted Discrimination

  method = 'dwdLinear'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)

Required packages: kerndwd

Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • weight (Class Weight)

Required packages: e1071

Oblique Random Forest

  method = 'ORFsvm'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Partial Least Squares

  method = 'kernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Polynomial Kernel Regularized Least Squares

  method = 'krlsPoly'

Type: Regression

Tuning parameters:

  • lambda (Regularization Parameter)
  • degree (Polynomial Degree)

Required packages: KRLS

Radial Basis Function Kernel Regularized Least Squares

  method = 'krlsRadial'

Type: Regression

Tuning parameters:

  • lambda (Regularization Parameter)
  • sigma (Sigma)

Required packages: KRLS, kernlab

Relevance Vector Machines with Linear Kernel

  method = 'rvmLinear'

Type: Regression

No tuning parameters for this model

Required packages: kernlab

Relevance Vector Machines with Polynomial Kernel

  method = 'rvmPoly'

Type: Regression

Tuning parameters:

  • scale (Scale)
  • degree (Polynomial Degree)

Required packages: kernlab

Relevance Vector Machines with Radial Basis Function Kernel

  method = 'rvmRadial'

Type: Regression

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

Support Vector Machines with Boundrange String Kernel

  method = 'svmBoundrangeString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Class Weights

  method = 'svmRadialWeights'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)
  • Weight (Weight)

Required packages: kernlab

Support Vector Machines with Exponential String Kernel

  method = 'svmExpoString'

Type: Regression, Classification

Tuning parameters:

  • lambda (lambda)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear2'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)

Required packages: e1071

Support Vector Machines with Polynomial Kernel

  method = 'svmPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialCost'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialSigma'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Notes: This SVM model tunes over the cost parameter and the RBF kernel parameter sigma. In the latter case, using tuneLength will, at most, evaluate six values of the kernel parameter. This enables a broad search over the cost parameter and a relatively narrow search over sigma

Support Vector Machines with Spectrum String Kernel

  method = 'svmSpectrumString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

7.0.19 L1 Regularization

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Bayesian Ridge Regression (Model Averaged)

  method = 'blassoAveraged'

Type: Regression

No tuning parameters for this model

Required packages: monomvn

Notes: This model makes predictions by averaging the predictions based on the posterior estimates of the regression coefficients. While it is possible that some of these posterior estimates are zero for non-informative predictors, the final predicted value may be a function of many (or even all) predictors.

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

Elasticnet

  method = 'enet'

Type: Regression

Tuning parameters:

  • fraction (Fraction of Full Solution)
  • lambda (Weight Decay)

Required packages: elasticnet

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

glmnet

  method = 'glmnet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: glmnet, Matrix

A model-specific variable importance metric is available.

Least Angle Regression

  method = 'lars'

Type: Regression

Tuning parameters:

  • fraction (Fraction)

Required packages: lars

Least Angle Regression

  method = 'lars2'

Type: Regression

Tuning parameters:

  • step (#Steps)

Required packages: lars

Multi-Step Adaptive MCP-Net

  method = 'msaenet'

Type: Regression, Classification

Tuning parameters:

  • alphas (Alpha)
  • nsteps (#Adaptive Estimation Steps)
  • scale (Adaptive Weight Scaling Factor)

Required packages: msaenet

A model-specific variable importance metric is available.

Non-Convex Penalized Quantile Regression

  method = 'rqnc'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)
  • penalty (Penalty Type)

Required packages: rqPen

Penalized Linear Discriminant Analysis

  method = 'PenalizedLDA'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • K (#Discriminant Functions)

Required packages: penalizedLDA, plyr

Penalized Linear Regression

  method = 'penalized'

Type: Regression

Tuning parameters:

  • lambda1 (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: penalized

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

Quantile Regression with LASSO penalty

  method = 'rqlasso'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)

Required packages: rqPen

Regularized Logistic Regression

  method = 'regLogistic'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • loss (Loss Function)
  • epsilon (Tolerance)

Required packages: LiblineaR

Relaxed Lasso

  method = 'relaxo'

Type: Regression

Tuning parameters:

  • lambda (Penalty Parameter)
  • phi (Relaxation Parameter)

Required packages: relaxo, plyr

Sparse Distance Weighted Discrimination

  method = 'sdwd'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: sdwd

A model-specific variable importance metric is available.

Sparse Linear Discriminant Analysis

  method = 'sparseLDA'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)

Required packages: sparseLDA

Sparse Mixture Discriminant Analysis

  method = 'smda'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)
  • R (# Subclasses)

Required packages: sparseLDA

Sparse Partial Least Squares

  method = 'spls'

Type: Regression, Classification

Tuning parameters:

  • K (#Components)
  • eta (Threshold)
  • kappa (Kappa)

Required packages: spls

The Bayesian lasso

  method = 'blasso'

Type: Regression

Tuning parameters:

  • sparsity (Sparsity Threshold)

Required packages: monomvn

Notes: This model creates predictions using the mean of the posterior distributions but sets some parameters specifically to zero based on the tuning parameter sparsity. For example, when sparsity = .5, only coefficients where at least half the posterior estimates are nonzero are used.

The lasso

  method = 'lasso'

Type: Regression

Tuning parameters:

  • fraction (Fraction of Full Solution)

Required packages: elasticnet

7.0.20 L2 Regularization

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Bayesian Ridge Regression

  method = 'bridge'

Type: Regression

No tuning parameters for this model

Required packages: monomvn

Distance Weighted Discrimination with Polynomial Kernel

  method = 'dwdPoly'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kerndwd

Distance Weighted Discrimination with Radial Basis Function Kernel

  method = 'dwdRadial'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • sigma (Sigma)

Required packages: kernlab, kerndwd

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

glmnet

  method = 'glmnet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: glmnet, Matrix

A model-specific variable importance metric is available.

Linear Distance Weighted Discrimination

  method = 'dwdLinear'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)

Required packages: kerndwd

Model Averaged Neural Network

  method = 'avNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)
  • bag (Bagging)

Required packages: nnet

Multi-Layer Perceptron

  method = 'mlpWeightDecay'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: RSNNS

Multi-Layer Perceptron, multiple layers

  method = 'mlpWeightDecayML'

Type: Regression, Classification

Tuning parameters:

  • layer1 (#Hidden Units layer1)
  • layer2 (#Hidden Units layer2)
  • layer3 (#Hidden Units layer3)
  • decay (Weight Decay)

Required packages: RSNNS

Multilayer Perceptron Network by Stochastic Gradient Descent

  method = 'mlpSGD'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • l2reg (L2 Regularization)
  • lambda (RMSE Gradient Scaling)
  • learn_rate (Learning Rate)
  • momentum (Momentum)
  • gamma (Learning Rate Decay)
  • minibatchsz (Batch Size)
  • repeats (#Models)

Required packages: FCNN4R, plyr

A model-specific variable importance metric is available.

Multilayer Perceptron Network with Weight Decay

  method = 'mlpKerasDecay'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • lambda (L2 Regularization)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations.

Multilayer Perceptron Network with Weight Decay

  method = 'mlpKerasDecayCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • lambda (L2 Regularization)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Multi-Step Adaptive MCP-Net

  method = 'msaenet'

Type: Regression, Classification

Tuning parameters:

  • alphas (Alpha)
  • nsteps (#Adaptive Estimation Steps)
  • scale (Adaptive Weight Scaling Factor)

Required packages: msaenet

A model-specific variable importance metric is available.

Neural Network

  method = 'nnet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Neural Networks with Feature Extraction

  method = 'pcaNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Penalized Linear Regression

  method = 'penalized'

Type: Regression

Tuning parameters:

  • lambda1 (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: penalized

Penalized Logistic Regression

  method = 'plr'

Type: Classification

Tuning parameters:

  • lambda (L2 Penalty)
  • cp (Complexity Parameter)

Required packages: stepPlr

Penalized Multinomial Regression

  method = 'multinom'

Type: Classification

Tuning parameters:

  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

Polynomial Kernel Regularized Least Squares

  method = 'krlsPoly'

Type: Regression

Tuning parameters:

  • lambda (Regularization Parameter)
  • degree (Polynomial Degree)

Required packages: KRLS

Quantile Regression Neural Network

  method = 'qrnn'

Type: Regression

Tuning parameters:

  • n.hidden (#Hidden Units)
  • penalty ( Weight Decay)
  • bag (Bagged Models?)

Required packages: qrnn

Radial Basis Function Kernel Regularized Least Squares

  method = 'krlsRadial'

Type: Regression

Tuning parameters:

  • lambda (Regularization Parameter)
  • sigma (Sigma)

Required packages: KRLS, kernlab

Radial Basis Function Network

  method = 'rbf'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)

Required packages: RSNNS

Radial Basis Function Network

  method = 'rbfDDA'

Type: Regression, Classification

Tuning parameters:

  • negativeThreshold (Activation Limit for Conflicting Classes)

Required packages: RSNNS

Regularized Logistic Regression

  method = 'regLogistic'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • loss (Loss Function)
  • epsilon (Tolerance)

Required packages: LiblineaR

Relaxed Lasso

  method = 'relaxo'

Type: Regression

Tuning parameters:

  • lambda (Penalty Parameter)
  • phi (Relaxation Parameter)

Required packages: relaxo, plyr

Ridge Regression

  method = 'ridge'

Type: Regression

Tuning parameters:

  • lambda (Weight Decay)

Required packages: elasticnet

Ridge Regression with Variable Selection

  method = 'foba'

Type: Regression

Tuning parameters:

  • k (#Variables Retained)
  • lambda (L2 Penalty)

Required packages: foba

Sparse Distance Weighted Discrimination

  method = 'sdwd'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: sdwd

A model-specific variable importance metric is available.

7.0.21 Linear Classifier

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Adjacent Categories Probability Model for Ordinal Data

  method = 'vglmAdjCat'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Bayesian Generalized Linear Model

  method = 'bayesglm'

Type: Regression, Classification

No tuning parameters for this model

Required packages: arm

Boosted Generalized Linear Model

  method = 'glmboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: plyr, mboost

A model-specific variable importance metric is available. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Continuation Ratio Model for Ordinal Data

  method = 'vglmContRatio'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Cumulative Probability Model for Ordinal Data

  method = 'vglmCumulative'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Diagonal Discriminant Analysis

  method = 'dda'

Type: Classification

Tuning parameters:

  • model (Model)
  • shrinkage (Shrinkage Type)

Required packages: sparsediscrim

Ensembles of Generalized Linear Models

  method = 'randomGLM'

Type: Regression, Classification

Tuning parameters:

  • maxInteractionOrder (Interaction Order)

Required packages: randomGLM

Factor-Based Linear Discriminant Analysis

  method = 'RFlda'

Type: Classification

Tuning parameters:

  • q (# Factors)

Required packages: HiDimDA

Gaussian Process

  method = 'gaussprLinear'

Type: Regression, Classification

No tuning parameters for this model

Required packages: kernlab

Generalized Linear Model

  method = 'glm'

Type: Regression, Classification

No tuning parameters for this model

A model-specific variable importance metric is available.

Generalized Linear Model with Stepwise Feature Selection

  method = 'glmStepAIC'

Type: Regression, Classification

No tuning parameters for this model

Required packages: MASS

Generalized Partial Least Squares

  method = 'gpls'

Type: Classification

Tuning parameters:

  • K.prov (#Components)

Required packages: gpls

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

glmnet

  method = 'glmnet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: glmnet, Matrix

A model-specific variable importance metric is available.

Heteroscedastic Discriminant Analysis

  method = 'hda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • newdim (Dimension of the Discriminative Subspace)

Required packages: hda

High Dimensional Discriminant Analysis

  method = 'hdda'

Type: Classification

Tuning parameters:

  • threshold (Threshold)
  • model (Model Type)

Required packages: HDclassif

High-Dimensional Regularized Discriminant Analysis

  method = 'hdrda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • shrinkage_type (Shrinkage Type)

Required packages: sparsediscrim

L2 Regularized Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights2'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)
  • weight (Class Weight)

Required packages: LiblineaR

L2 Regularized Support Vector Machine (dual) with Linear Kernel

  method = 'svmLinear3'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)

Required packages: LiblineaR

Least Squares Support Vector Machine

  method = 'lssvmLinear'

Type: Classification

Tuning parameters:

  • tau (Regularization Parameter)

Required packages: kernlab

Linear Discriminant Analysis

  method = 'lda'

Type: Classification

No tuning parameters for this model

Required packages: MASS

Linear Discriminant Analysis

  method = 'lda2'

Type: Classification

Tuning parameters:

  • dimen (#Discriminant Functions)

Required packages: MASS

Linear Discriminant Analysis with Stepwise Feature Selection

  method = 'stepLDA'

Type: Classification

Tuning parameters:

  • maxvar (Maximum #Variables)
  • direction (Search Direction)

Required packages: klaR, MASS

Linear Distance Weighted Discrimination

  method = 'dwdLinear'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)

Required packages: kerndwd

Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • weight (Class Weight)

Required packages: e1071

Localized Linear Discriminant Analysis

  method = 'loclda'

Type: Classification

Tuning parameters:

  • k (#Nearest Neighbors)

Required packages: klaR

Logic Regression

  method = 'logreg'

Type: Regression, Classification

Tuning parameters:

  • treesize (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: LogicReg

Logistic Model Trees

  method = 'LMT'

Type: Classification

Tuning parameters:

  • iter (# Iteratons)

Required packages: RWeka

Maximum Uncertainty Linear Discriminant Analysis

  method = 'Mlda'

Type: Classification

No tuning parameters for this model

Required packages: HiDimDA

Multi-Step Adaptive MCP-Net

  method = 'msaenet'

Type: Regression, Classification

Tuning parameters:

  • alphas (Alpha)
  • nsteps (#Adaptive Estimation Steps)
  • scale (Adaptive Weight Scaling Factor)

Required packages: msaenet

A model-specific variable importance metric is available.

Nearest Shrunken Centroids

  method = 'pam'

Type: Classification

Tuning parameters:

  • threshold (Shrinkage Threshold)

Required packages: pamr

A model-specific variable importance metric is available.

Ordered Logistic or Probit Regression

  method = 'polr'

Type: Classification

Tuning parameters:

  • method (parameter)

Required packages: MASS

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'kernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'pls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'simpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'widekernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Penalized Linear Discriminant Analysis

  method = 'PenalizedLDA'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • K (#Discriminant Functions)

Required packages: penalizedLDA, plyr

Penalized Logistic Regression

  method = 'plr'

Type: Classification

Tuning parameters:

  • lambda (L2 Penalty)
  • cp (Complexity Parameter)

Required packages: stepPlr

Penalized Multinomial Regression

  method = 'multinom'

Type: Classification

Tuning parameters:

  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

Regularized Discriminant Analysis

  method = 'rda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)

Required packages: klaR

Regularized Linear Discriminant Analysis

  method = 'rlda'

Type: Classification

Tuning parameters:

  • estimator (Regularization Method)

Required packages: sparsediscrim

Regularized Logistic Regression

  method = 'regLogistic'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • loss (Loss Function)
  • epsilon (Tolerance)

Required packages: LiblineaR

Robust Linear Discriminant Analysis

  method = 'Linda'

Type: Classification

No tuning parameters for this model

Required packages: rrcov

Robust Regularized Linear Discriminant Analysis

  method = 'rrlda'

Type: Classification

Tuning parameters:

  • lambda (Penalty Parameter)
  • hp (Robustness Parameter)
  • penalty (Penalty Type)

Required packages: rrlda

Robust SIMCA

  method = 'RSimca'

Type: Classification

No tuning parameters for this model

Required packages: rrcovHD

Shrinkage Discriminant Analysis

  method = 'sda'

Type: Classification

Tuning parameters:

  • diagonal (Diagonalize)
  • lambda (shrinkage)

Required packages: sda

Sparse Distance Weighted Discrimination

  method = 'sdwd'

Type: Classification

Tuning parameters:

  • lambda (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: sdwd

A model-specific variable importance metric is available.

Sparse Linear Discriminant Analysis

  method = 'sparseLDA'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)

Required packages: sparseLDA

Sparse Partial Least Squares

  method = 'spls'

Type: Regression, Classification

Tuning parameters:

  • K (#Components)
  • eta (Threshold)
  • kappa (Kappa)

Required packages: spls

Stabilized Linear Discriminant Analysis

  method = 'slda'

Type: Classification

No tuning parameters for this model

Required packages: ipred

Support Vector Machines with Linear Kernel

  method = 'svmLinear'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear2'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)

Required packages: e1071

7.0.22 Linear Regression

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Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Bayesian Ridge Regression

  method = 'bridge'

Type: Regression

No tuning parameters for this model

Required packages: monomvn

Bayesian Ridge Regression (Model Averaged)

  method = 'blassoAveraged'

Type: Regression

No tuning parameters for this model

Required packages: monomvn

Notes: This model makes predictions by averaging the predictions based on the posterior estimates of the regression coefficients. While it is possible that some of these posterior estimates are zero for non-informative predictors, the final predicted value may be a function of many (or even all) predictors.

Boosted Linear Model

  method = 'BstLm'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • nu (Shrinkage)

Required packages: bst, plyr

Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

Elasticnet

  method = 'enet'

Type: Regression

Tuning parameters:

  • fraction (Fraction of Full Solution)
  • lambda (Weight Decay)

Required packages: elasticnet

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

glmnet

  method = 'glmnet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: glmnet, Matrix

A model-specific variable importance metric is available.

Independent Component Regression

  method = 'icr'

Type: Regression

Tuning parameters:

  • n.comp (#Components)

Required packages: fastICA

L2 Regularized Support Vector Machine (dual) with Linear Kernel

  method = 'svmLinear3'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)

Required packages: LiblineaR

Least Angle Regression

  method = 'lars'

Type: Regression

Tuning parameters:

  • fraction (Fraction)

Required packages: lars

Least Angle Regression

  method = 'lars2'

Type: Regression

Tuning parameters:

  • step (#Steps)

Required packages: lars

Linear Regression

  method = 'lm'

Type: Regression

Tuning parameters:

  • intercept (intercept)

A model-specific variable importance metric is available.

Linear Regression with Backwards Selection

  method = 'leapBackward'

Type: Regression

Tuning parameters:

  • nvmax (Maximum Number of Predictors)

Required packages: leaps

Linear Regression with Forward Selection

  method = 'leapForward'

Type: Regression

Tuning parameters:

  • nvmax (Maximum Number of Predictors)

Required packages: leaps

Linear Regression with Stepwise Selection

  method = 'leapSeq'

Type: Regression

Tuning parameters:

  • nvmax (Maximum Number of Predictors)

Required packages: leaps

Linear Regression with Stepwise Selection

  method = 'lmStepAIC'

Type: Regression

No tuning parameters for this model

Required packages: MASS

Logic Regression

  method = 'logreg'

Type: Regression, Classification

Tuning parameters:

  • treesize (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: LogicReg

Model Rules

  method = 'M5Rules'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)

Required packages: RWeka

Model Tree

  method = 'M5'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)
  • rules (Rules)

Required packages: RWeka

Multi-Step Adaptive MCP-Net

  method = 'msaenet'

Type: Regression, Classification

Tuning parameters:

  • alphas (Alpha)
  • nsteps (#Adaptive Estimation Steps)
  • scale (Adaptive Weight Scaling Factor)

Required packages: msaenet

A model-specific variable importance metric is available.

Non-Convex Penalized Quantile Regression

  method = 'rqnc'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)
  • penalty (Penalty Type)

Required packages: rqPen

Non-Negative Least Squares

  method = 'nnls'

Type: Regression

No tuning parameters for this model

Required packages: nnls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'kernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'pls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'simpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'widekernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Penalized Linear Regression

  method = 'penalized'

Type: Regression

Tuning parameters:

  • lambda1 (L1 Penalty)
  • lambda2 (L2 Penalty)

Required packages: penalized

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

Principal Component Analysis

  method = 'pcr'

Type: Regression

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

Quantile Regression with LASSO penalty

  method = 'rqlasso'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)

Required packages: rqPen

Relaxed Lasso

  method = 'relaxo'

Type: Regression

Tuning parameters:

  • lambda (Penalty Parameter)
  • phi (Relaxation Parameter)

Required packages: relaxo, plyr

Relevance Vector Machines with Linear Kernel

  method = 'rvmLinear'

Type: Regression

No tuning parameters for this model

Required packages: kernlab

Ridge Regression

  method = 'ridge'

Type: Regression

Tuning parameters:

  • lambda (Weight Decay)

Required packages: elasticnet

Ridge Regression with Variable Selection

  method = 'foba'

Type: Regression

Tuning parameters:

  • k (#Variables Retained)
  • lambda (L2 Penalty)

Required packages: foba

Robust Linear Model

  method = 'rlm'

Type: Regression

Tuning parameters:

  • intercept (intercept)
  • psi (psi)

Required packages: MASS

Sparse Partial Least Squares

  method = 'spls'

Type: Regression, Classification

Tuning parameters:

  • K (#Components)
  • eta (Threshold)
  • kappa (Kappa)

Required packages: spls

Spike and Slab Regression

  method = 'spikeslab'

Type: Regression

Tuning parameters:

  • vars (Variables Retained)

Required packages: spikeslab, plyr

Supervised Principal Component Analysis

  method = 'superpc'

Type: Regression

Tuning parameters:

  • threshold (Threshold)
  • n.components (#Components)

Required packages: superpc

Support Vector Machines with Linear Kernel

  method = 'svmLinear'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear2'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)

Required packages: e1071

The Bayesian lasso

  method = 'blasso'

Type: Regression

Tuning parameters:

  • sparsity (Sparsity Threshold)

Required packages: monomvn

Notes: This model creates predictions using the mean of the posterior distributions but sets some parameters specifically to zero based on the tuning parameter sparsity. For example, when sparsity = .5, only coefficients where at least half the posterior estimates are nonzero are used.

The lasso

  method = 'lasso'

Type: Regression

Tuning parameters:

  • fraction (Fraction of Full Solution)

Required packages: elasticnet

7.0.23 Logic Regression

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Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Logic Regression

  method = 'logreg'

Type: Regression, Classification

Tuning parameters:

  • treesize (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: LogicReg

7.0.24 Logistic Regression

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Adjacent Categories Probability Model for Ordinal Data

  method = 'vglmAdjCat'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Bayesian Generalized Linear Model

  method = 'bayesglm'

Type: Regression, Classification

No tuning parameters for this model

Required packages: arm

Boosted Logistic Regression

  method = 'LogitBoost'

Type: Classification

Tuning parameters:

  • nIter (# Boosting Iterations)

Required packages: caTools

Continuation Ratio Model for Ordinal Data

  method = 'vglmContRatio'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Cumulative Probability Model for Ordinal Data

  method = 'vglmCumulative'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Generalized Partial Least Squares

  method = 'gpls'

Type: Classification

Tuning parameters:

  • K.prov (#Components)

Required packages: gpls

Logic Regression

  method = 'logreg'

Type: Regression, Classification

Tuning parameters:

  • treesize (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: LogicReg

Logistic Model Trees

  method = 'LMT'

Type: Classification

Tuning parameters:

  • iter (# Iteratons)

Required packages: RWeka

Oblique Random Forest

  method = 'ORFlog'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Ordered Logistic or Probit Regression

  method = 'polr'

Type: Classification

Tuning parameters:

  • method (parameter)

Required packages: MASS

A model-specific variable importance metric is available.

Penalized Logistic Regression

  method = 'plr'

Type: Classification

Tuning parameters:

  • lambda (L2 Penalty)
  • cp (Complexity Parameter)

Required packages: stepPlr

Penalized Multinomial Regression

  method = 'multinom'

Type: Classification

Tuning parameters:

  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

7.0.25 Mixture Model

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Adaptive Mixture Discriminant Analysis

  method = 'amdai'

Type: Classification

Tuning parameters:

  • model (Model Type)

Required packages: adaptDA

Mixture Discriminant Analysis

  method = 'mda'

Type: Classification

Tuning parameters:

  • subclasses (#Subclasses Per Class)

Required packages: mda

Robust Mixture Discriminant Analysis

  method = 'rmda'

Type: Classification

Tuning parameters:

  • K (#Subclasses Per Class)
  • model (Model)

Required packages: robustDA

Sparse Mixture Discriminant Analysis

  method = 'smda'

Type: Classification

Tuning parameters:

  • NumVars (# Predictors)
  • lambda (Lambda)
  • R (# Subclasses)

Required packages: sparseLDA

7.0.26 Model Tree

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Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

Logistic Model Trees

  method = 'LMT'

Type: Classification

Tuning parameters:

  • iter (# Iteratons)

Required packages: RWeka

Model Rules

  method = 'M5Rules'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)

Required packages: RWeka

Model Tree

  method = 'M5'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)
  • rules (Rules)

Required packages: RWeka

7.0.27 Multivariate Adaptive Regression Splines

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Bagged Flexible Discriminant Analysis

  method = 'bagFDA'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Bagged MARS

  method = 'bagEarth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Bagged MARS using gCV Pruning

  method = 'bagEarthGCV'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Flexible Discriminant Analysis

  method = 'fda'

Type: Classification

Tuning parameters:

  • degree (Product Degree)
  • nprune (#Terms)

Required packages: earth, mda

A model-specific variable importance metric is available.

Multivariate Adaptive Regression Spline

  method = 'earth'

Type: Regression, Classification

Tuning parameters:

  • nprune (#Terms)
  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

Multivariate Adaptive Regression Splines

  method = 'gcvEarth'

Type: Regression, Classification

Tuning parameters:

  • degree (Product Degree)

Required packages: earth

A model-specific variable importance metric is available.

7.0.28 Neural Network

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Bayesian Regularized Neural Networks

  method = 'brnn'

Type: Regression

Tuning parameters:

  • neurons (# Neurons)

Required packages: brnn

Extreme Learning Machine

  method = 'elm'

Type: Classification, Regression

Tuning parameters:

  • nhid (#Hidden Units)
  • actfun (Activation Function)

Required packages: elmNN

Model Averaged Neural Network

  method = 'avNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)
  • bag (Bagging)

Required packages: nnet

Monotone Multi-Layer Perceptron Neural Network

  method = 'monmlp'

Type: Classification, Regression

Tuning parameters:

  • hidden1 (#Hidden Units)
  • n.ensemble (#Models)

Required packages: monmlp

Multi-Layer Perceptron

  method = 'mlp'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)

Required packages: RSNNS

Multi-Layer Perceptron

  method = 'mlpWeightDecay'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: RSNNS

Multi-Layer Perceptron, multiple layers

  method = 'mlpWeightDecayML'

Type: Regression, Classification

Tuning parameters:

  • layer1 (#Hidden Units layer1)
  • layer2 (#Hidden Units layer2)
  • layer3 (#Hidden Units layer3)
  • decay (Weight Decay)

Required packages: RSNNS

Multilayer Perceptron Network by Stochastic Gradient Descent

  method = 'mlpSGD'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • l2reg (L2 Regularization)
  • lambda (RMSE Gradient Scaling)
  • learn_rate (Learning Rate)
  • momentum (Momentum)
  • gamma (Learning Rate Decay)
  • minibatchsz (Batch Size)
  • repeats (#Models)

Required packages: FCNN4R, plyr

A model-specific variable importance metric is available.

Multilayer Perceptron Network with Dropout

  method = 'mlpKerasDropout'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • dropout (Dropout Rate)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations.

Multilayer Perceptron Network with Dropout

  method = 'mlpKerasDropoutCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • dropout (Dropout Rate)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Multilayer Perceptron Network with Weight Decay

  method = 'mlpKerasDecay'

Type: Regression, Classification

Tuning parameters:

  • size (#Hidden Units)
  • lambda (L2 Regularization)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations.

Multilayer Perceptron Network with Weight Decay

  method = 'mlpKerasDecayCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • lambda (L2 Regularization)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Multi-Layer Perceptron, with multiple layers

  method = 'mlpML'

Type: Regression, Classification

Tuning parameters:

  • layer1 (#Hidden Units layer1)
  • layer2 (#Hidden Units layer2)
  • layer3 (#Hidden Units layer3)

Required packages: RSNNS

Neural Network

  method = 'mxnet'

Type: Classification, Regression

Tuning parameters:

  • layer1 (#Hidden Units in Layer 1)
  • layer2 (#Hidden Units in Layer 2)
  • layer3 (#Hidden Units in Layer 3)
  • learning.rate (Learning Rate)
  • momentum (Momentum)
  • dropout (Dropout Rate)
  • activation (Activation Function)

Required packages: mxnet

Notes: The mxnet package is not yet on CRAN. See http://mxnet.io for installation instructions.

Neural Network

  method = 'mxnetAdam'

Type: Classification, Regression

Tuning parameters:

  • layer1 (#Hidden Units in Layer 1)
  • layer2 (#Hidden Units in Layer 2)
  • layer3 (#Hidden Units in Layer 3)
  • dropout (Dropout Rate)
  • beta1 (beta1)
  • beta2 (beta2)
  • learningrate (Learning Rate)
  • activation (Activation Function)

Required packages: mxnet

Notes: The mxnet package is not yet on CRAN. See http://mxnet.io for installation instructions. Users are strongly advised to define num.round themselves.

Neural Network

  method = 'neuralnet'

Type: Regression

Tuning parameters:

  • layer1 (#Hidden Units in Layer 1)
  • layer2 (#Hidden Units in Layer 2)
  • layer3 (#Hidden Units in Layer 3)

Required packages: neuralnet

Neural Network

  method = 'nnet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Neural Networks with Feature Extraction

  method = 'pcaNNet'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)
  • decay (Weight Decay)

Required packages: nnet

Penalized Multinomial Regression

  method = 'multinom'

Type: Classification

Tuning parameters:

  • decay (Weight Decay)

Required packages: nnet

A model-specific variable importance metric is available.

Quantile Regression Neural Network

  method = 'qrnn'

Type: Regression

Tuning parameters:

  • n.hidden (#Hidden Units)
  • penalty ( Weight Decay)
  • bag (Bagged Models?)

Required packages: qrnn

Radial Basis Function Network

  method = 'rbf'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)

Required packages: RSNNS

Radial Basis Function Network

  method = 'rbfDDA'

Type: Regression, Classification

Tuning parameters:

  • negativeThreshold (Activation Limit for Conflicting Classes)

Required packages: RSNNS

Stacked AutoEncoder Deep Neural Network

  method = 'dnn'

Type: Classification, Regression

Tuning parameters:

  • layer1 (Hidden Layer 1)
  • layer2 (Hidden Layer 2)
  • layer3 (Hidden Layer 3)
  • hidden_dropout (Hidden Dropouts)
  • visible_dropout (Visible Dropout)

Required packages: deepnet

7.0.29 Oblique Tree

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Oblique Random Forest

  method = 'ORFlog'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFpls'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFsvm'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

7.0.30 Ordinal Outcomes

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Adjacent Categories Probability Model for Ordinal Data

  method = 'vglmAdjCat'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

CART or Ordinal Responses

  method = 'rpartScore'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • split (Split Function)
  • prune (Pruning Measure)

Required packages: rpartScore, plyr

A model-specific variable importance metric is available.

Continuation Ratio Model for Ordinal Data

  method = 'vglmContRatio'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Cumulative Probability Model for Ordinal Data

  method = 'vglmCumulative'

Type: Classification

Tuning parameters:

  • parallel (Parallel Curves)
  • link (Link Function)

Required packages: VGAM

Ordered Logistic or Probit Regression

  method = 'polr'

Type: Classification

Tuning parameters:

  • method (parameter)

Required packages: MASS

A model-specific variable importance metric is available.

Penalized Ordinal Regression

  method = 'ordinalNet'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • criteria (Selection Criterion)
  • link (Link Function)

Required packages: ordinalNet, plyr

A model-specific variable importance metric is available. Notes: Requires ordinalNet package version >= 2.0

7.0.31 Partial Least Squares

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Generalized Partial Least Squares

  method = 'gpls'

Type: Classification

Tuning parameters:

  • K.prov (#Components)

Required packages: gpls

Oblique Random Forest

  method = 'ORFpls'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Partial Least Squares

  method = 'kernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'pls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'simpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares

  method = 'widekernelpls'

Type: Regression, Classification

Tuning parameters:

  • ncomp (#Components)

Required packages: pls

A model-specific variable importance metric is available.

Partial Least Squares Generalized Linear Models

  method = 'plsRglm'

Type: Classification, Regression

Tuning parameters:

  • nt (#PLS Components)
  • alpha.pvals.expli (p-Value threshold)

Required packages: plsRglm

Sparse Partial Least Squares

  method = 'spls'

Type: Regression, Classification

Tuning parameters:

  • K (#Components)
  • eta (Threshold)
  • kappa (Kappa)

Required packages: spls

7.0.32 Patient Rule Induction Method

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Patient Rule Induction Method

  method = 'PRIM'

Type: Classification

Tuning parameters:

  • peel.alpha (peeling quantile)
  • paste.alpha (pasting quantile)
  • mass.min (minimum mass)

Required packages: supervisedPRIM

7.0.33 Polynomial Model

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Diagonal Discriminant Analysis

  method = 'dda'

Type: Classification

Tuning parameters:

  • model (Model)
  • shrinkage (Shrinkage Type)

Required packages: sparsediscrim

Distance Weighted Discrimination with Polynomial Kernel

  method = 'dwdPoly'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kerndwd

Gaussian Process with Polynomial Kernel

  method = 'gaussprPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kernlab

High-Dimensional Regularized Discriminant Analysis

  method = 'hdrda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • shrinkage_type (Shrinkage Type)

Required packages: sparsediscrim

Least Squares Support Vector Machine with Polynomial Kernel

  method = 'lssvmPoly'

Type: Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • tau (Regularization Parameter)

Required packages: kernlab

Penalized Discriminant Analysis

  method = 'pda'

Type: Classification

Tuning parameters:

  • lambda (Shrinkage Penalty Coefficient)

Required packages: mda

Penalized Discriminant Analysis

  method = 'pda2'

Type: Classification

Tuning parameters:

  • df (Degrees of Freedom)

Required packages: mda

Polynomial Kernel Regularized Least Squares

  method = 'krlsPoly'

Type: Regression

Tuning parameters:

  • lambda (Regularization Parameter)
  • degree (Polynomial Degree)

Required packages: KRLS

Quadratic Discriminant Analysis

  method = 'qda'

Type: Classification

No tuning parameters for this model

Required packages: MASS

Quadratic Discriminant Analysis with Stepwise Feature Selection

  method = 'stepQDA'

Type: Classification

Tuning parameters:

  • maxvar (Maximum #Variables)
  • direction (Search Direction)

Required packages: klaR, MASS

Regularized Discriminant Analysis

  method = 'rda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)

Required packages: klaR

Regularized Linear Discriminant Analysis

  method = 'rlda'

Type: Classification

Tuning parameters:

  • estimator (Regularization Method)

Required packages: sparsediscrim

Relevance Vector Machines with Polynomial Kernel

  method = 'rvmPoly'

Type: Regression

Tuning parameters:

  • scale (Scale)
  • degree (Polynomial Degree)

Required packages: kernlab

Robust Quadratic Discriminant Analysis

  method = 'QdaCov'

Type: Classification

No tuning parameters for this model

Required packages: rrcov

Support Vector Machines with Polynomial Kernel

  method = 'svmPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • C (Cost)

Required packages: kernlab

7.0.34 Prototype Models

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Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

Greedy Prototype Selection

  method = 'protoclass'

Type: Classification

Tuning parameters:

  • eps (Ball Size)
  • Minkowski (Distance Order)

Required packages: proxy, protoclass

k-Nearest Neighbors

  method = 'kknn'

Type: Regression, Classification

Tuning parameters:

  • kmax (Max. #Neighbors)
  • distance (Distance)
  • kernel (Kernel)

Required packages: kknn

k-Nearest Neighbors

  method = 'knn'

Type: Classification, Regression

Tuning parameters:

  • k (#Neighbors)

Knn regression via sklearn.neighbors.KNeighborsRegressor

  method = 'pythonKnnReg'

Type: Regression

Tuning parameters:

  • n_neighbors (#Neighbors)
  • weights (Weight Function)
  • algorithm (Algorithm)
  • leaf_size (Leaf Size)
  • metric (Distance Metric)
  • p (p)

Required packages: rPython

Learning Vector Quantization

  method = 'lvq'

Type: Classification

Tuning parameters:

  • size (Codebook Size)
  • k (#Prototypes)

Required packages: class

Nearest Shrunken Centroids

  method = 'pam'

Type: Classification

Tuning parameters:

  • threshold (Shrinkage Threshold)

Required packages: pamr

A model-specific variable importance metric is available.

Optimal Weighted Nearest Neighbor Classifier

  method = 'ownn'

Type: Classification

Tuning parameters:

  • K (#Neighbors)

Required packages: snn

Stabilized Nearest Neighbor Classifier

  method = 'snn'

Type: Classification

Tuning parameters:

  • lambda (Stabilization Parameter)

Required packages: snn

7.0.35 Quantile Regression

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Non-Convex Penalized Quantile Regression

  method = 'rqnc'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)
  • penalty (Penalty Type)

Required packages: rqPen

Quantile Random Forest

  method = 'qrf'

Type: Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: quantregForest

Quantile Regression Neural Network

  method = 'qrnn'

Type: Regression

Tuning parameters:

  • n.hidden (#Hidden Units)
  • penalty ( Weight Decay)
  • bag (Bagged Models?)

Required packages: qrnn

Quantile Regression with LASSO penalty

  method = 'rqlasso'

Type: Regression

Tuning parameters:

  • lambda (L1 Penalty)

Required packages: rqPen

7.0.36 Radial Basis Function

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Distance Weighted Discrimination with Radial Basis Function Kernel

  method = 'dwdRadial'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • sigma (Sigma)

Required packages: kernlab, kerndwd

Gaussian Process with Radial Basis Function Kernel

  method = 'gaussprRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

Least Squares Support Vector Machine with Radial Basis Function Kernel

  method = 'lssvmRadial'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • tau (Regularization Parameter)

Required packages: kernlab

Radial Basis Function Kernel Regularized Least Squares

  method = 'krlsRadial'

Type: Regression

Tuning parameters:

  • lambda (Regularization Parameter)
  • sigma (Sigma)

Required packages: KRLS, kernlab

Radial Basis Function Network

  method = 'rbf'

Type: Classification, Regression

Tuning parameters:

  • size (#Hidden Units)

Required packages: RSNNS

Radial Basis Function Network

  method = 'rbfDDA'

Type: Regression, Classification

Tuning parameters:

  • negativeThreshold (Activation Limit for Conflicting Classes)

Required packages: RSNNS

Relevance Vector Machines with Radial Basis Function Kernel

  method = 'rvmRadial'

Type: Regression

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

Support Vector Machines with Class Weights

  method = 'svmRadialWeights'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)
  • Weight (Weight)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialCost'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialSigma'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Notes: This SVM model tunes over the cost parameter and the RBF kernel parameter sigma. In the latter case, using tuneLength will, at most, evaluate six values of the kernel parameter. This enables a broad search over the cost parameter and a relatively narrow search over sigma

Variational Bayesian Multinomial Probit Regression

  method = 'vbmpRadial'

Type: Classification

Tuning parameters:

  • estimateTheta (Theta Estimated)

Required packages: vbmp

7.0.37 Random Forest

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Conditional Inference Random Forest

  method = 'cforest'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: party

A model-specific variable importance metric is available.

Oblique Random Forest

  method = 'ORFlog'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFpls'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFsvm'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Parallel Random Forest

  method = 'parRF'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: e1071, randomForest, foreach, import

A model-specific variable importance metric is available.

Quantile Random Forest

  method = 'qrf'

Type: Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: quantregForest

Random Ferns

  method = 'rFerns'

Type: Classification

Tuning parameters:

  • depth (Fern Depth)

Required packages: rFerns

Random Forest

  method = 'ranger'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • splitrule (Splitting Rule)

Required packages: e1071, ranger, dplyr

A model-specific variable importance metric is available.

Random Forest

  method = 'Rborist'

Type: Classification, Regression

Tuning parameters:

  • predFixed (#Randomly Selected Predictors)

Required packages: Rborist

A model-specific variable importance metric is available.

Random Forest

  method = 'rf'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: randomForest

A model-specific variable importance metric is available.

Random Forest by Randomization

  method = 'extraTrees'

Type: Regression, Classification

Tuning parameters:

  • mtry (# Randomly Selected Predictors)
  • numRandomCuts (# Random Cuts)

Required packages: extraTrees

Random Forest Rule-Based Model

  method = 'rfRules'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • maxdepth (Maximum Rule Depth)

Required packages: randomForest, inTrees, plyr

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRF'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)
  • coefImp (Importance Coefficient)

Required packages: randomForest, RRF

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRFglobal'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)

Required packages: RRF

A model-specific variable importance metric is available.

Weighted Subspace Random Forest

  method = 'wsrf'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: wsrf

7.0.38 Regularization

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Bayesian Regularized Neural Networks

  method = 'brnn'

Type: Regression

Tuning parameters:

  • neurons (# Neurons)

Required packages: brnn

Diagonal Discriminant Analysis

  method = 'dda'

Type: Classification

Tuning parameters:

  • model (Model)
  • shrinkage (Shrinkage Type)

Required packages: sparsediscrim

Heteroscedastic Discriminant Analysis

  method = 'hda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • newdim (Dimension of the Discriminative Subspace)

Required packages: hda

High-Dimensional Regularized Discriminant Analysis

  method = 'hdrda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)
  • shrinkage_type (Shrinkage Type)

Required packages: sparsediscrim

Regularized Discriminant Analysis

  method = 'rda'

Type: Classification

Tuning parameters:

  • gamma (Gamma)
  • lambda (Lambda)

Required packages: klaR

Regularized Linear Discriminant Analysis

  method = 'rlda'

Type: Classification

Tuning parameters:

  • estimator (Regularization Method)

Required packages: sparsediscrim

Regularized Random Forest

  method = 'RRF'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)
  • coefImp (Importance Coefficient)

Required packages: randomForest, RRF

A model-specific variable importance metric is available.

Regularized Random Forest

  method = 'RRFglobal'

Type: Regression, Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • coefReg (Regularization Value)

Required packages: RRF

A model-specific variable importance metric is available.

Robust Regularized Linear Discriminant Analysis

  method = 'rrlda'

Type: Classification

Tuning parameters:

  • lambda (Penalty Parameter)
  • hp (Robustness Parameter)
  • penalty (Penalty Type)

Required packages: rrlda

Shrinkage Discriminant Analysis

  method = 'sda'

Type: Classification

Tuning parameters:

  • diagonal (Diagonalize)
  • lambda (shrinkage)

Required packages: sda

7.0.39 Relevance Vector Machines

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Relevance Vector Machines with Linear Kernel

  method = 'rvmLinear'

Type: Regression

No tuning parameters for this model

Required packages: kernlab

Relevance Vector Machines with Polynomial Kernel

  method = 'rvmPoly'

Type: Regression

Tuning parameters:

  • scale (Scale)
  • degree (Polynomial Degree)

Required packages: kernlab

Relevance Vector Machines with Radial Basis Function Kernel

  method = 'rvmRadial'

Type: Regression

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

7.0.40 Ridge Regression

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Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Ridge Regression with Variable Selection

  method = 'foba'

Type: Regression

Tuning parameters:

  • k (#Variables Retained)
  • lambda (L2 Penalty)

Required packages: foba

7.0.41 Robust Methods

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L2 Regularized Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights2'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)
  • weight (Class Weight)

Required packages: LiblineaR

L2 Regularized Support Vector Machine (dual) with Linear Kernel

  method = 'svmLinear3'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)

Required packages: LiblineaR

Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • weight (Class Weight)

Required packages: e1071

Regularized Logistic Regression

  method = 'regLogistic'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • loss (Loss Function)
  • epsilon (Tolerance)

Required packages: LiblineaR

Relevance Vector Machines with Linear Kernel

  method = 'rvmLinear'

Type: Regression

No tuning parameters for this model

Required packages: kernlab

Relevance Vector Machines with Polynomial Kernel

  method = 'rvmPoly'

Type: Regression

Tuning parameters:

  • scale (Scale)
  • degree (Polynomial Degree)

Required packages: kernlab

Relevance Vector Machines with Radial Basis Function Kernel

  method = 'rvmRadial'

Type: Regression

Tuning parameters:

  • sigma (Sigma)

Required packages: kernlab

Robust Mixture Discriminant Analysis

  method = 'rmda'

Type: Classification

Tuning parameters:

  • K (#Subclasses Per Class)
  • model (Model)

Required packages: robustDA

Support Vector Machines with Boundrange String Kernel

  method = 'svmBoundrangeString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Exponential String Kernel

  method = 'svmExpoString'

Type: Regression, Classification

Tuning parameters:

  • lambda (lambda)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear2'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)

Required packages: e1071

Support Vector Machines with Polynomial Kernel

  method = 'svmPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialSigma'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Notes: This SVM model tunes over the cost parameter and the RBF kernel parameter sigma. In the latter case, using tuneLength will, at most, evaluate six values of the kernel parameter. This enables a broad search over the cost parameter and a relatively narrow search over sigma

Support Vector Machines with Spectrum String Kernel

  method = 'svmSpectrumString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

7.0.42 Robust Model

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Quantile Random Forest

  method = 'qrf'

Type: Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: quantregForest

Quantile Regression Neural Network

  method = 'qrnn'

Type: Regression

Tuning parameters:

  • n.hidden (#Hidden Units)
  • penalty ( Weight Decay)
  • bag (Bagged Models?)

Required packages: qrnn

Robust Linear Discriminant Analysis

  method = 'Linda'

Type: Classification

No tuning parameters for this model

Required packages: rrcov

Robust Linear Model

  method = 'rlm'

Type: Regression

Tuning parameters:

  • intercept (intercept)
  • psi (psi)

Required packages: MASS

Robust Regularized Linear Discriminant Analysis

  method = 'rrlda'

Type: Classification

Tuning parameters:

  • lambda (Penalty Parameter)
  • hp (Robustness Parameter)
  • penalty (Penalty Type)

Required packages: rrlda

Robust SIMCA

  method = 'RSimca'

Type: Classification

No tuning parameters for this model

Required packages: rrcovHD

SIMCA

  method = 'CSimca'

Type: Classification

No tuning parameters for this model

Required packages: rrcov, rrcovHD

7.0.43 ROC Curves

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ROC-Based Classifier

  method = 'rocc'

Type: Classification

Tuning parameters:

  • xgenes (#Variables Retained)

Required packages: rocc

7.0.44 Rule-Based Model

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Adaptive-Network-Based Fuzzy Inference System

  method = 'ANFIS'

Type: Regression

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • max.iter (Max. Iterations)

Required packages: frbs

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cubist

  method = 'cubist'

Type: Regression

Tuning parameters:

  • committees (#Committees)
  • neighbors (#Instances)

Required packages: Cubist

A model-specific variable importance metric is available.

Dynamic Evolving Neural-Fuzzy Inference System

  method = 'DENFIS'

Type: Regression

Tuning parameters:

  • Dthr (Threshold)
  • max.iter (Max. Iterations)

Required packages: frbs

Fuzzy Inference Rules by Descent Method

  method = 'FIR.DM'

Type: Regression

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • max.iter (Max. Iterations)

Required packages: frbs

Fuzzy Rules Using Chi’s Method

  method = 'FRBCS.CHI'

Type: Classification

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • type.mf (Membership Function)

Required packages: frbs

Fuzzy Rules Using Genetic Cooperative-Competitive Learning

  method = 'GFS.GCCL'

Type: Classification

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • popu.size (Population Size)
  • max.gen (Max. Generations)

Required packages: frbs

Fuzzy Rules Using Genetic Cooperative-Competitive Learning and Pittsburgh

  method = 'FH.GBML'

Type: Classification

Tuning parameters:

  • max.num.rule (Max. #Rules)
  • popu.size (Population Size)
  • max.gen (Max. Generations)

Required packages: frbs

Fuzzy Rules Using the Structural Learning Algorithm on Vague Environment

  method = 'SLAVE'

Type: Classification

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • max.iter (Max. Iterations)
  • max.gen (Max. Generations)

Required packages: frbs

Fuzzy Rules via MOGUL

  method = 'GFS.FR.MOGUL'

Type: Regression

Tuning parameters:

  • max.gen (Max. Generations)
  • max.iter (Max. Iterations)
  • max.tune (Max. Tuning Iterations)

Required packages: frbs

Fuzzy Rules via Thrift

  method = 'GFS.THRIFT'

Type: Regression

Tuning parameters:

  • popu.size (Population Size)
  • num.labels (# Fuzzy Labels)
  • max.gen (Max. Generations)

Required packages: frbs

Fuzzy Rules with Weight Factor

  method = 'FRBCS.W'

Type: Classification

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • type.mf (Membership Function)

Required packages: frbs

Genetic Lateral Tuning and Rule Selection of Linguistic Fuzzy Systems

  method = 'GFS.LT.RS'

Type: Regression

Tuning parameters:

  • popu.size (Population Size)
  • num.labels (# Fuzzy Labels)
  • max.gen (Max. Generations)

Required packages: frbs

Hybrid Neural Fuzzy Inference System

  method = 'HYFIS'

Type: Regression

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • max.iter (Max. Iterations)

Required packages: frbs

Model Rules

  method = 'M5Rules'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)

Required packages: RWeka

Model Tree

  method = 'M5'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)
  • rules (Rules)

Required packages: RWeka

Patient Rule Induction Method

  method = 'PRIM'

Type: Classification

Tuning parameters:

  • peel.alpha (peeling quantile)
  • paste.alpha (pasting quantile)
  • mass.min (minimum mass)

Required packages: supervisedPRIM

Random Forest Rule-Based Model

  method = 'rfRules'

Type: Classification, Regression

Tuning parameters:

  • mtry (#Randomly Selected Predictors)
  • maxdepth (Maximum Rule Depth)

Required packages: randomForest, inTrees, plyr

A model-specific variable importance metric is available.

Rule-Based Classifier

  method = 'JRip'

Type: Classification

Tuning parameters:

  • NumOpt (# Optimizations)
  • NumFolds (# Folds)
  • MinWeights (Min Weights)

Required packages: RWeka

A model-specific variable importance metric is available.

Rule-Based Classifier

  method = 'PART'

Type: Classification

Tuning parameters:

  • threshold (Confidence Threshold)
  • pruned (Pruning)

Required packages: RWeka

A model-specific variable importance metric is available.

Simplified TSK Fuzzy Rules

  method = 'FS.HGD'

Type: Regression

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • max.iter (Max. Iterations)

Required packages: frbs

Single C5.0 Ruleset

  method = 'C5.0Rules'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Single Rule Classification

  method = 'OneR'

Type: Classification

No tuning parameters for this model

Required packages: RWeka

Subtractive Clustering and Fuzzy c-Means Rules

  method = 'SBC'

Type: Regression

Tuning parameters:

  • r.a (Radius)
  • eps.high (Upper Threshold)
  • eps.low (Lower Threshold)

Required packages: frbs

Wang and Mendel Fuzzy Rules

  method = 'WM'

Type: Regression

Tuning parameters:

  • num.labels (#Fuzzy Terms)
  • type.mf (Membership Function)

Required packages: frbs

7.0.45 Self-Organising Maps

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Self-Organizing Maps

  method = 'xyf'

Type: Classification, Regression

Tuning parameters:

  • xdim (Rows)
  • ydim (Columns)
  • user.weights (Layer Weight)
  • topo (Topology)

Required packages: kohonen

Notes: As of version 3.0.0 of the kohonen package, the argument user.weights replaces the old alpha parameter. user.weights is usually a vector of relative weights such as c(1, 3) but is parameterized here as a proportion such as c(1-.75, .75) where the .75 is the value of the tuning parameter passed to train and indicates that the outcome layer has 3 times the weight as the predictor layer.

7.0.46 String Kernel

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Support Vector Machines with Boundrange String Kernel

  method = 'svmBoundrangeString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Exponential String Kernel

  method = 'svmExpoString'

Type: Regression, Classification

Tuning parameters:

  • lambda (lambda)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Spectrum String Kernel

  method = 'svmSpectrumString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

7.0.47 Support Vector Machines

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L2 Regularized Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights2'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)
  • weight (Class Weight)

Required packages: LiblineaR

L2 Regularized Support Vector Machine (dual) with Linear Kernel

  method = 'svmLinear3'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)

Required packages: LiblineaR

Least Squares Support Vector Machine

  method = 'lssvmLinear'

Type: Classification

Tuning parameters:

  • tau (Regularization Parameter)

Required packages: kernlab

Least Squares Support Vector Machine with Polynomial Kernel

  method = 'lssvmPoly'

Type: Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • tau (Regularization Parameter)

Required packages: kernlab

Least Squares Support Vector Machine with Radial Basis Function Kernel

  method = 'lssvmRadial'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • tau (Regularization Parameter)

Required packages: kernlab

Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • weight (Class Weight)

Required packages: e1071

Support Vector Machines with Boundrange String Kernel

  method = 'svmBoundrangeString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Class Weights

  method = 'svmRadialWeights'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)
  • Weight (Weight)

Required packages: kernlab

Support Vector Machines with Exponential String Kernel

  method = 'svmExpoString'

Type: Regression, Classification

Tuning parameters:

  • lambda (lambda)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Linear Kernel

  method = 'svmLinear2'

Type: Regression, Classification

Tuning parameters:

  • cost (Cost)

Required packages: e1071

Support Vector Machines with Polynomial Kernel

  method = 'svmPoly'

Type: Regression, Classification

Tuning parameters:

  • degree (Polynomial Degree)
  • scale (Scale)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadial'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialCost'

Type: Regression, Classification

Tuning parameters:

  • C (Cost)

Required packages: kernlab

Support Vector Machines with Radial Basis Function Kernel

  method = 'svmRadialSigma'

Type: Regression, Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)

Required packages: kernlab

Notes: This SVM model tunes over the cost parameter and the RBF kernel parameter sigma. In the latter case, using tuneLength will, at most, evaluate six values of the kernel parameter. This enables a broad search over the cost parameter and a relatively narrow search over sigma

Support Vector Machines with Spectrum String Kernel

  method = 'svmSpectrumString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

7.0.48 Text Mining

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Support Vector Machines with Boundrange String Kernel

  method = 'svmBoundrangeString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Exponential String Kernel

  method = 'svmExpoString'

Type: Regression, Classification

Tuning parameters:

  • lambda (lambda)
  • C (Cost)

Required packages: kernlab

Support Vector Machines with Spectrum String Kernel

  method = 'svmSpectrumString'

Type: Regression, Classification

Tuning parameters:

  • length (length)
  • C (Cost)

Required packages: kernlab

7.0.49 Tree-Based Model

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AdaBoost Classification Trees

  method = 'adaboost'

Type: Classification

Tuning parameters:

  • nIter (#Trees)
  • method (Method)

Required packages: fastAdaboost

AdaBoost.M1

  method = 'AdaBoost.M1'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)
  • coeflearn (Coefficient Type)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged AdaBoost

  method = 'AdaBag'

Type: Classification

Tuning parameters:

  • mfinal (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: adabag, plyr

A model-specific variable importance metric is available.

Bagged CART

  method = 'treebag'

Type: Regression, Classification

No tuning parameters for this model

Required packages: ipred, plyr, e1071

A model-specific variable importance metric is available.

Bayesian Additive Regression Trees

  method = 'bartMachine'

Type: Classification, Regression

Tuning parameters:

  • num_trees (#Trees)
  • k (Prior Boundary)
  • alpha (Base Terminal Node Hyperparameter)
  • beta (Power Terminal Node Hyperparameter)
  • nu (Degrees of Freedom)

Required packages: bartMachine

A model-specific variable importance metric is available.

Boosted Classification Trees

  method = 'ada'

Type: Classification

Tuning parameters:

  • iter (#Trees)
  • maxdepth (Max Tree Depth)
  • nu (Learning Rate)

Required packages: ada, plyr

Boosted Logistic Regression

  method = 'LogitBoost'

Type: Classification

Tuning parameters:

  • nIter (# Boosting Iterations)

Required packages: caTools

Boosted Tree

  method = 'blackboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (#Trees)
  • maxdepth (Max Tree Depth)

Required packages: party, mboost, plyr

Boosted Tree

  method = 'bstTree'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • maxdepth (Max Tree Depth)
  • nu (Shrinkage)

Required packages: bst, plyr

C4.5-like Trees

  method = 'J48'

Type: Classification

Tuning parameters:

  • C (Confidence Threshold)
  • M (Minimum Instances Per Leaf)

Required packages: RWeka

C5.0

  method = 'C5.0'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)

Required packages: C50, plyr

A model-specific variable importance metric is available.

CART

  method = 'rpart'

Type: Regression, Classification

Tuning parameters:

  • cp (Complexity Parameter)

Required packages: rpart

A model-specific variable importance metric is available.

CART

  method = 'rpart1SE'

Type: Regression, Classification

No tuning parameters for this model

Required packages: rpart

A model-specific variable importance metric is available. Notes: This CART model replicates the same process used by the rpart function where the model complexity is determined using the one-standard error method. This procedure is replicated inside of the resampling done by train so that an external resampling estimate can be obtained.

CART

  method = 'rpart2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)

Required packages: rpart

A model-specific variable importance metric is available.

CART or Ordinal Responses

  method = 'rpartScore'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • split (Split Function)
  • prune (Pruning Measure)

Required packages: rpartScore, plyr

A model-specific variable importance metric is available.

CHi-squared Automated Interaction Detection

  method = 'chaid'

Type: Classification

Tuning parameters:

  • alpha2 (Merging Threshold)
  • alpha3 (Splitting former Merged Threshold)
  • alpha4 ( Splitting former Merged Threshold)

Required packages: CHAID

Conditional Inference Tree

  method = 'ctree'

Type: Classification, Regression

Tuning parameters:

  • mincriterion (1 - P-Value Threshold)

Required packages: party

Conditional Inference Tree

  method = 'ctree2'

Type: Regression, Classification

Tuning parameters:

  • maxdepth (Max Tree Depth)
  • mincriterion (1 - P-Value Threshold)

Required packages: party

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive CART

  method = 'rpartCost'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • Cost (Cost)

Required packages: rpart, plyr

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

eXtreme Gradient Boosting

  method = 'xgbTree'

Type: Regression, Classification

Tuning parameters:

  • nrounds (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • eta (Shrinkage)
  • gamma (Minimum Loss Reduction)
  • colsample_bytree (Subsample Ratio of Columns)
  • min_child_weight (Minimum Sum of Instance Weight)
  • subsample (Subsample Percentage)

Required packages: xgboost, plyr

A model-specific variable importance metric is available.

Gradient Boosting Machines

  method = 'gbm_h2o'

Type: Regression, Classification

Tuning parameters:

  • ntrees (# Boosting Iterations)
  • max_depth (Max Tree Depth)
  • min_rows (Min. Terminal Node Size)
  • learn_rate (Shrinkage)
  • col_sample_rate (#Randomly Selected Predictors)

Required packages: h2o

A model-specific variable importance metric is available.

Model Tree

  method = 'M5'

Type: Regression

Tuning parameters:

  • pruned (Pruned)
  • smoothed (Smoothed)
  • rules (Rules)

Required packages: RWeka

Rotation Forest

  method = 'rotationForest'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)

Required packages: rotationForest

A model-specific variable importance metric is available.

Rotation Forest

  method = 'rotationForestCp'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)
  • cp (Complexity Parameter)

Required packages: rpart, plyr, rotationForest

A model-specific variable importance metric is available.

Single C5.0 Tree

  method = 'C5.0Tree'

Type: Classification

No tuning parameters for this model

Required packages: C50

A model-specific variable importance metric is available.

Stochastic Gradient Boosting

  method = 'gbm'

Type: Regression, Classification

Tuning parameters:

  • n.trees (# Boosting Iterations)
  • interaction.depth (Max Tree Depth)
  • shrinkage (Shrinkage)
  • n.minobsinnode (Min. Terminal Node Size)

Required packages: gbm, plyr

A model-specific variable importance metric is available.

Tree-Based Ensembles

  method = 'nodeHarvest'

Type: Regression, Classification

Tuning parameters:

  • maxinter (Maximum Interaction Depth)
  • mode (Prediction Mode)

Required packages: nodeHarvest

Tree Models from Genetic Algorithms

  method = 'evtree'

Type: Regression, Classification

Tuning parameters:

  • alpha (Complexity Parameter)

Required packages: evtree

7.0.50 Two Class Only

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AdaBoost Classification Trees

  method = 'adaboost'

Type: Classification

Tuning parameters:

  • nIter (#Trees)
  • method (Method)

Required packages: fastAdaboost

Bagged Logic Regression

  method = 'logicBag'

Type: Regression, Classification

Tuning parameters:

  • nleaves (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: logicFS

Bayesian Additive Regression Trees

  method = 'bartMachine'

Type: Classification, Regression

Tuning parameters:

  • num_trees (#Trees)
  • k (Prior Boundary)
  • alpha (Base Terminal Node Hyperparameter)
  • beta (Power Terminal Node Hyperparameter)
  • nu (Degrees of Freedom)

Required packages: bartMachine

A model-specific variable importance metric is available.

Binary Discriminant Analysis

  method = 'binda'

Type: Classification

Tuning parameters:

  • lambda.freqs (Shrinkage Intensity)

Required packages: binda

Boosted Classification Trees

  method = 'ada'

Type: Classification

Tuning parameters:

  • iter (#Trees)
  • maxdepth (Max Tree Depth)
  • nu (Learning Rate)

Required packages: ada, plyr

Boosted Generalized Additive Model

  method = 'gamboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: mboost, plyr, import

Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

Boosted Generalized Linear Model

  method = 'glmboost'

Type: Regression, Classification

Tuning parameters:

  • mstop (# Boosting Iterations)
  • prune (AIC Prune?)

Required packages: plyr, mboost

A model-specific variable importance metric is available. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. See the examples in ?mboost::mstop. If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value.

CHi-squared Automated Interaction Detection

  method = 'chaid'

Type: Classification

Tuning parameters:

  • alpha2 (Merging Threshold)
  • alpha3 (Splitting former Merged Threshold)
  • alpha4 ( Splitting former Merged Threshold)

Required packages: CHAID

Cost-Sensitive C5.0

  method = 'C5.0Cost'

Type: Classification

Tuning parameters:

  • trials (# Boosting Iterations)
  • model (Model Type)
  • winnow (Winnow)
  • cost (Cost)

Required packages: C50, plyr

A model-specific variable importance metric is available.

Cost-Sensitive CART

  method = 'rpartCost'

Type: Classification

Tuning parameters:

  • cp (Complexity Parameter)
  • Cost (Cost)

Required packages: rpart, plyr

DeepBoost

  method = 'deepboost'

Type: Classification

Tuning parameters:

  • num_iter (# Boosting Iterations)
  • tree_depth (Tree Depth)
  • beta (L1 Regularization)
  • lambda (Tree Depth Regularization)
  • loss_type (Loss)

Required packages: deepboost

Distance Weighted Discrimination with Polynomial Kernel

  method = 'dwdPoly'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • degree (Polynomial Degree)
  • scale (Scale)

Required packages: kerndwd

Distance Weighted Discrimination with Radial Basis Function Kernel

  method = 'dwdRadial'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)
  • sigma (Sigma)

Required packages: kernlab, kerndwd

Generalized Linear Model

  method = 'glm'

Type: Regression, Classification

No tuning parameters for this model

A model-specific variable importance metric is available.

Generalized Linear Model with Stepwise Feature Selection

  method = 'glmStepAIC'

Type: Regression, Classification

No tuning parameters for this model

Required packages: MASS

glmnet

  method = 'glmnet_h2o'

Type: Regression, Classification

Tuning parameters:

  • alpha (Mixing Percentage)
  • lambda (Regularization Parameter)

Required packages: h2o

A model-specific variable importance metric is available.

L2 Regularized Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights2'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • Loss (Loss Function)
  • weight (Class Weight)

Required packages: LiblineaR

Linear Distance Weighted Discrimination

  method = 'dwdLinear'

Type: Classification

Tuning parameters:

  • lambda (Regularization Parameter)
  • qval (q)

Required packages: kerndwd

Linear Support Vector Machines with Class Weights

  method = 'svmLinearWeights'

Type: Classification

Tuning parameters:

  • cost (Cost)
  • weight (Class Weight)

Required packages: e1071

Logic Regression

  method = 'logreg'

Type: Regression, Classification

Tuning parameters:

  • treesize (Maximum Number of Leaves)
  • ntrees (Number of Trees)

Required packages: LogicReg

Multilayer Perceptron Network with Dropout

  method = 'mlpKerasDropoutCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • dropout (Dropout Rate)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Multilayer Perceptron Network with Weight Decay

  method = 'mlpKerasDecayCost'

Type: Classification

Tuning parameters:

  • size (#Hidden Units)
  • lambda (L2 Regularization)
  • batch_size (Batch Size)
  • lr (Learning Rate)
  • rho (Rho)
  • decay (Learning Rate Decay)
  • cost (Cost)
  • activation (Activation Function)

Required packages: keras

Notes: After train completes, the keras model object is serialized so that it can be used between R session. When predicting, the code will temporarily unsearalize the object. To make the predictions more efficient, the user might want to use keras::unsearlize_model(object$finalModel$object) in the current R session so that that operation is only done once. Also, this model cannot be run in parallel due to the nature of how tensorflow does the computations. Finally, the cost parameter weights the first class in the outcome vector.

Oblique Random Forest

  method = 'ORFlog'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFpls'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFridge'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Oblique Random Forest

  method = 'ORFsvm'

Type: Classification

Tuning parameters:

  • mtry (#Randomly Selected Predictors)

Required packages: obliqueRF

Partial Least Squares Generalized Linear Models

  method = 'plsRglm'

Type: Classification, Regression

Tuning parameters:

  • nt (#PLS Components)
  • alpha.pvals.expli (p-Value threshold)

Required packages: plsRglm

Rotation Forest

  method = 'rotationForest'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)

Required packages: rotationForest

A model-specific variable importance metric is available.

Rotation Forest

  method = 'rotationForestCp'

Type: Classification

Tuning parameters:

  • K (#Variable Subsets)
  • L (Ensemble Size)
  • cp (Complexity Parameter)

Required packages: rpart, plyr, rotationForest

A model-specific variable importance metric is available.

Support Vector Machines with Class Weights

  method = 'svmRadialWeights'

Type: Classification

Tuning parameters:

  • sigma (Sigma)
  • C (Cost)
  • Weight (Weight)

Required packages: kernlab

Tree-Based Ensembles

  method = 'nodeHarvest'

Type: Regression, Classification

Tuning parameters:

  • maxinter (Maximum Interaction Depth)
  • mode (Prediction Mode)

Required packages: nodeHarvest