Applied Machine Learning for Tabular Data

Authors

Max Kuhn

Kjell Johnson

Published

2025-04-20

Preface

To run this reprex, you should only need:

pak::pak(
  c("glue", "gt", "gtExtras", "knitr", "patchwork", "scales", "sessioninfo",
    "tidymodels")
)

Computing Notes

Quarto was used to compile and render the materials

Quarto 1.7.27
[✓] Checking environment information...
[✓] Checking versions of quarto binary dependencies...
      Pandoc version 3.6.3: OK
      Dart Sass version 1.85.1: OK
      Deno version 1.46.3: OK
      Typst version 0.13.0: OK
[✓] Checking versions of quarto dependencies......OK
[✓] Checking Quarto installation......OK
      Version: 1.7.27
[✓] Checking tools....................OK
      TinyTeX: (external install)
      Chromium: (not installed)
[✓] Checking LaTeX....................OK
      Using: TinyTex
      Version: 2024
[✓] Checking Chrome Headless....................OK
      Using: Chrome found on system
      Source: MacOS known location
[✓] Checking basic markdown render....OK
[✓] Checking Python 3 installation....OK
      Version: 3.9.18
      Jupyter: (None)
      Jupyter is not available in this Python installation.
[✓] Checking R installation...........OK
      Version: 4.4.2
      LibPaths:
        - /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
      knitr: 1.49
      rmarkdown: 2.27
[✓] Checking Knitr engine render......OK

R version 4.4.2 (2024-10-31) was used for the majority of the computations. torch 2.5.1 was also used. The versions of the primary R modeling and visualization packages used here are:

`aorsf` (0.1.5)	`applicable` (0.1.1)	`aspline` (0.2.0)
`baguette` (1.1.0)	`bestNormalize` (1.9.1)	`bibtex` (0.5.1)
`bonsai` (0.3.2)	`broom` (1.0.8)	`broom.mixed` (0.2.9.5)
`brulee` (0.4.0.9000)	`bslib` (0.7.0)	`C50` (0.1.8)
`colino` (0.0.1)	`Cubist` (0.4.4)	`DALEXtra` (2.3.0)
`dbarts` (0.9-30)	`ddalpha` (1.3.15)	`desirability2` (0.0.1)
`dials` (1.4.0)	`dimRed` (0.2.6)	`discrim` (1.0.1)
`doMC` (1.3.8)	`dplyr` (1.1.4.9000)	`e1071` (1.7-14)
`earth` (5.3.4)	`embed` (1.1.5)	`emmeans` (1.11.0)
`fastICA` (1.2-5.1)	`finetune` (1.2.0)	`forested` (0.1.0)
`GA` (3.2.4)	`gganimate` (1.0.9)	`ggforce` (0.4.2)
`ggiraph` (0.8.9)	`ggplot2` (3.5.2)	`glmnet` (4.1-8)
`gt` (0.10.1)	`hardhat` (1.4.1)	`heatmaply` (1.5.0)
`hstats` (1.2.2)	`ipred` (0.9-15)	`irlba` (2.3.5.1)
`janitor` (2.2.0)	`kernlab` (0.9-33)	`kknn` (1.3.1)
`klaR` (1.7-3)	`leaflet` (2.2.2)	`lightgbm` (4.5.0)
`lme4` (1.1-35.5)	`Matrix` (1.7-1)	`mda` (0.5-5)
`measure` (0.0.1.9000)	`mgcv` (1.9-1)	`mixdir` (0.3.0)
`mixOmics` (6.31.4)	`modeldata` (1.4.0)	`modeldatatoo` (0.3.0)
`naniar` (1.1.0)	`pamr` (1.56.2)	`parsnip` (1.3.1)
`partykit` (1.2-22)	`patchwork` (1.3.0)	`plsmod` (1.0.0.9000)
`probably` (1.0.3)	`pROC` (1.18.5)	`purrr` (1.0.4.9000)
`ragg` (1.3.1)	`ranger` (0.17.0)	`recipes` (1.3.0)
`rpart` (4.1.24)	`rsample` (1.3.0)	`RSpectra` (0.16-2)
`rstudioapi` (0.17.1)	`rules` (1.0.2)	`sf` (1.0-20)
`sfd` (0.1.0)	`shinylive` (0.1.1)	`sparsediscrim` (0.3.0)
`sparseLDA` (0.1-9)	`sparsevctrs` (0.3.3)	`spatialsample` (0.6.0)
`splines2` (0.5.3)	`stacks` (1.0.5)	`stopwords` (2.3)
`textrecipes` (1.1.0)	`themis` (1.0.3)	`tidymodels` (1.2.0)
`tidyposterior` (1.0.1)	`tidyr` (1.3.1.9000)	`tidysdm` (0.9.5)
`torch` (0.14.2)	`tune` (1.3.0)	`usethis` (2.2.3)
`uwot` (0.2.2)	`VBsparsePCA` (0.1.0)	`viridis` (0.6.5)
`workflows` (1.2.0)	`workflowsets` (1.1.0)	`xgboost` (1.7.8.1)
`xrf` (0.2.2)	`yardstick` (1.3.2)

Preface

Computing Notes

Chapter References