Introduction to Machine Learning with tidymodels

Max Kuhn and Simon Couch

👋 Who am I?

👋 Who are we?

👋 Who are you?

You have a good working knowledge of R, and be familiar with basic data wrangling and visualisation as described in R for Data Science by Wickham and Grolemund (2016).

sources: https://github.com/topepo/2024_FDA_Mod_and_Sim

slides: https://topepo.github.io/2024_FDA_Mod_and_Sim

What is tidymodels?

The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles.

- tidymodels.org

…so what is modeling and machine learning?

BYO Venn Diagram

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

Think about the modeling problem, not the syntax.

The Meta-package

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom        1.0.6     ✔ recipes      1.1.0
#> ✔ dials        1.3.0     ✔ rsample      1.2.1
#> ✔ dplyr        1.1.4     ✔ tibble       3.2.1
#> ✔ ggplot2      3.5.1     ✔ tidyr        1.3.1
#> ✔ infer        1.0.7     ✔ tune         1.2.1
#> ✔ modeldata    1.4.0     ✔ workflows    1.1.4
#> ✔ parsnip      1.2.1     ✔ workflowsets 1.1.0
#> ✔ purrr        1.0.2     ✔ yardstick    1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()
#> • Learn how to get started at https://www.tidymodels.org/start/

tidymodels_prefer()

Why tidymodels?

Why tidymodels? Consistency

How many different ways can you think of to fit a linear model in R?

The blessing:

Many statistical modeling practitioners implement methods in R

The curse:

Many statistical modeling practitioners implement methods in R

Why tidymodels? Consistency

mtcars
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear
#> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4
#> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4
#> Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4
#>                     carb
#> Mazda RX4              4
#> Mazda RX4 Wag          4
#> Datsun 710             1
#> Hornet 4 Drive         1
#> Hornet Sportabout      2
#> Valiant                1
#> Duster 360             4
#> Merc 240D              2
#> Merc 230               2
#> Merc 280               4
#> Merc 280C              4
#> Merc 450SE             3
#> Merc 450SL             3
#> Merc 450SLC            3
#> Cadillac Fleetwood     4
#> Lincoln Continental    4
#> Chrysler Imperial      4
#> Fiat 128               1
#> Honda Civic            2
#> Toyota Corolla         1
#> Toyota Corona          1
#> Dodge Challenger       2
#> AMC Javelin            2
#> Camaro Z28             4
#> Pontiac Firebird       2
#> Fiat X1-9              1
#> Porsche 914-2          2
#> Lotus Europa           2
#> Ford Pantera L         4
#> Ferrari Dino           6
#> Maserati Bora          8
#> Volvo 142E             2

Why tidymodels? Consistency

With lm():

model <- 
  lm(mpg ~ ., mtcars)

With tidymodels:

model <-
  linear_reg() %>%
  set_engine("lm") %>%
  fit(mpg ~ ., mtcars)

Why tidymodels? Consistency

With glmnet:

model <- 
  glmnet(
    as.matrix(mtcars[2:11]),
    mtcars$mpg
  )

With tidymodels:

model <-
  linear_reg() %>%
  set_engine("glmnet") %>%
  fit(mpg ~ ., mtcars)

Why tidymodels? Consistency

With h2o:

h2o.init()
as.h2o(mtcars, "cars")

model <- 
  h2o.glm(
    x = colnames(mtcars[2:11]), 
    y = "mpg",
    "cars"
  )

With tidymodels:

model <-
  linear_reg() %>%
  set_engine("h2o") %>%
  fit(mpg ~ ., mtcars)

Why tidymodels? Consistency

Why tidymodels? Safety¹

Overfitting leads to analysts believing models are more performant than they actually are.

A 2023 review found data leakage to be “a widespread failure mode in machine-learning (ML)-based science.”

Implementations of the same machine learning model give differing results, resulting in irreproducibility of modeling results.

Why tidymodels? Safety

Some of the resistance I’ve seen to tidymodels comes from a place of “This makes it too easy- you’re not thinking carefully about what the code is doing!” But I think this is getting it backwards.

By removing the burden of writing procedural logic, I get to focus on scientific and statistical questions about my data and model.

David Robinson

Why tidymodels? Completeness

Built-in support for 99 machine learning models!

#> # A tibble: 99 × 2
#>    name       engine   
#>    <chr>      <chr>    
#>  1 boost_tree C5.0     
#>  2 boost_tree h2o      
#>  3 boost_tree h2o_gbm  
#>  4 boost_tree lightgbm 
#>  5 boost_tree mboost   
#>  6 boost_tree spark    
#>  7 boost_tree xgboost  
#>  8 null_model parsnip  
#>  9 svm_linear LiblineaR
#> 10 svm_linear kernlab  
#> # ℹ 89 more rows

Why tidymodels? Completeness

Built-in support for 102 data pre-processing techniques!

#> # A tibble: 102 × 1
#>    name                   
#>    <chr>                  
#>  1 step_bagimpute         
#>  2 step_classdist_shrunken
#>  3 step_impute_mode       
#>  4 add_step               
#>  5 step                   
#>  6 print_step             
#>  7 step_dummy             
#>  8 step_window            
#>  9 step_nnmf              
#> 10 step_intercept         
#> # ℹ 92 more rows

Why tidymodels? Extensibility

Can’t find the technique you need?

Why tidymodels? Extensibility

Why tidymodels? Deployability

Tightly integrated with Posit Workbench and Connect

Workbench: scalable, on-demand computational resources
Connect: share work with collaborators and practitioners

The vetiver package makes it extremely easy to publish your model.

Applied example 🍕🍕

Food deliveries

These data are in the modeldata package.

glimpse(deliveries)
#> Rows: 10,012
#> Columns: 31
#> $ time_to_delivery <dbl> 16.1106, 22.9466, 30.2882, 33.4266, 27.2255…
#> $ hour             <dbl> 11.899, 19.230, 18.374, 15.836, 19.619, 12.…
#> $ day              <fct> Thu, Tue, Fri, Thu, Fri, Sat, Sun, Thu, Fri…
#> $ distance         <dbl> 3.15, 3.69, 2.06, 5.97, 2.52, 3.35, 2.46, 2…
#> $ item_01          <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0…
#> $ item_02          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0…
#> $ item_03          <int> 2, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1…
#> $ item_04          <int> 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0…
#> $ item_05          <int> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_06          <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
#> $ item_07          <int> 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0…
#> $ item_08          <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0…
#> $ item_09          <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1…
#> $ item_10          <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
#> $ item_11          <int> 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_12          <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
#> $ item_13          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_14          <int> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_15          <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
#> $ item_16          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_17          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
#> $ item_18          <int> 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
#> $ item_19          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_20          <int> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_21          <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0…
#> $ item_22          <int> 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0…
#> $ item_23          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_24          <int> 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ item_25          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0…
#> $ item_26          <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0…
#> $ item_27          <int> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0…

Investigate the data!

Let’s take 10m and have you explore the data.

Let me know what you think about them. (not a trick question)

10:00

Splitting the data

We typically start by splitting the data into partitions used for modeling and assessment:

set.seed(991)
delivery_split <- initial_split(deliveries, prop = 0.75, strata = time_to_delivery)


delivery_train <- training(delivery_split)
delivery_test  <- testing(delivery_split)

(TMwR) (aml4td)

Splitting the data (with validation)

Instead, let’s do a 3-way split to include a validation set that we can use before the test set:

set.seed(991)
delivery_split <- initial_validation_split(deliveries, prop = c(0.8, 0.1), 
                                           strata = time_to_delivery)

delivery_train <- training(delivery_split)
delivery_test  <- testing(delivery_split)
delivery_val   <- validation(delivery_split)

(TMwR)

A linear model

mod_spec <- linear_reg()
mod_fit <- mod_spec %>% fit(time_to_delivery ~ ., data = delivery_train)

tidy(mod_fit)
#> # A tibble: 36 × 5
#>    term        estimate std.error statistic   p.value
#>    <chr>          <dbl>     <dbl>     <dbl>     <dbl>
#>  1 (Intercept)   -18.4     0.372     -49.4  0        
#>  2 hour            1.78    0.0169    105.   0        
#>  3 dayTue          1.02    0.254       4.02 5.77e-  5
#>  4 dayWed          3.46    0.230      15.1  9.52e- 51
#>  5 dayThu          5.04    0.225      22.4  9.71e-108
#>  6 dayFri          6.98    0.220      31.7  1.49e-207
#>  7 daySat          7.82    0.219      35.7  2.81e-259
#>  8 daySun          3.53    0.229      15.4  5.15e- 53
#>  9 distance        2.78    0.0414     67.0  0        
#> 10 item_01         1.61    0.140      11.5  2.13e- 30
#> # ℹ 26 more rows

A linear model

# Good: 
predict(mod_fit, head(delivery_val, 3))
#> # A tibble: 3 × 1
#>   .pred
#>   <dbl>
#> 1  12.4
#> 2  27.2
#> 3  20.2

# Better:
augment(mod_fit, head(delivery_val, 3)) %>% select(1:9)
#> # A tibble: 3 × 9
#>   .pred .resid time_to_delivery  hour day   distance item_01 item_02
#>   <dbl>  <dbl>            <dbl> <dbl> <fct>    <dbl>   <int>   <int>
#> 1  12.4   5.62             18.0  12.1 Tue       2.4        0       0
#> 2  27.2   4.31             31.5  16.5 Sat       2.57       0       0
#> 3  20.2  -2.42             17.8  13.2 Fri       2.74       0       0
#> # ℹ 1 more variable: item_03 <int>

Does it work?

augment(mod_fit, delivery_val) %>% 
  ggplot(aes(.pred, time_to_delivery)) + 
  geom_abline(col = "green", lty = 2) +
  geom_point(alpha = 1 / 3) +
  geom_smooth(se = FALSE) +
  coord_obs_pred()

Meh.

How will we quantify “meh”?

Measuring performance

There are many metrics that we can use. Let’s go with RMSE and R²:

reg_metrics <- metric_set(rmse, rsq)

augment(mod_fit, delivery_val) %>% 
  reg_metrics(time_to_delivery, .pred)
#> # A tibble: 2 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 rmse    standard       3.76 
#> 2 rsq     standard       0.704

(TMwR)

Feature engineering

There’s a lot more we can do with this model.

We need better representations of our predictors.

We need recipes! 🧁

Recipes are sequential operations for feature engineering.

(TMwR)

Initializing the recipe

food_rec <- recipe(time_to_delivery ~ ., data = delivery_train)
food_rec

This first call just catalogs the data.

summary(food_rec)
#> # A tibble: 31 × 4
#>    variable type      role      source  
#>    <chr>    <list>    <chr>     <chr>   
#>  1 hour     <chr [2]> predictor original
#>  2 day      <chr [3]> predictor original
#>  3 distance <chr [2]> predictor original
#>  4 item_01  <chr [2]> predictor original
#>  5 item_02  <chr [2]> predictor original
#>  6 item_03  <chr [2]> predictor original
#>  7 item_04  <chr [2]> predictor original
#>  8 item_05  <chr [2]> predictor original
#>  9 item_06  <chr [2]> predictor original
#> 10 item_07  <chr [2]> predictor original
#> # ℹ 21 more rows

Converting to indicator variables

food_rec <- recipe(time_to_delivery ~ ., data = delivery_train) %>% 
  step_dummy(all_factor_predictors())

For any factor predictors, make binary indicators.

There are a lot of recipe steps that can convert categorical predictors to numeric columns (TMwR).

The new columns for day will have default names of day_Tue - day_Sun.

There are many types of recipe selectors.

Splines are the best

Splines are tools that enable linear models to model nonlinear data.

We choose the type of spline function and how many features to use.

More features, more flexibility
More features, more risk of overfitting

We’ll focus on natural splines.

(TMwR)

#| label: fig-natural-spline
#| viewerHeight: 600
#| viewerWidth: "100%"
#| standalone: true


library(shiny)
library(patchwork)
library(dplyr)
library(tidyr)
library(ggplot2)
library(splines2)
library(bslib)
library(viridis)
library(modeldata)

data(ames)
ames$Sale_Price <- log10(ames$Sale_Price)

ui <- page_fillable(
  theme = bs_theme(bg = "#fcfefe", fg = "#595959"),
  padding = "1rem",
  layout_columns(
    fill = FALSE,
    col_widths = breakpoints(sm = c(-3, 6, -3)),
    sliderInput(
      "spline_df",
      label = "# Spline Terms",
      min = 3L, max = 30L, step = 2L, value = 3L
    ), # sliderInput
  ), # layout_columns
  layout_columns(
    fill = FALSE,
    col_widths = breakpoints(sm = c(-1, 10, -1)),
    as_fill_carrier(plotOutput('spline'))
  )         
)

server <- function(input, output, session) {
  
  light_bg <- "#fcfefe" # from aml4td.scss
  grid_theme <- bs_theme(
    bg = "#fcfefe", fg = "#595959"
  )
  
  # ------------------------------------------------------------------------------
  
  theme_light_bl<- function(...) {
    
    ret <- ggplot2::theme_bw(...)
    
    col_rect <- ggplot2::element_rect(fill = light_bg, colour = light_bg)
    ret$panel.background  <- col_rect
    ret$plot.background   <- col_rect
    ret$legend.background <- col_rect
    ret$legend.key        <- col_rect
    
    larger_x_text <- ggplot2::element_text(size = rel(1.25))
    larger_y_text <- ggplot2::element_text(size = rel(1.25), angle = 90)
    ret$axis.text.x <- larger_x_text
    ret$axis.text.y <- larger_y_text
    ret$axis.title.x <- larger_x_text
    ret$axis.title.y <- larger_y_text  
    
    ret$legend.position <- "top"
    
    ret
  }
  
  col_rect <- ggplot2::element_rect(fill = light_bg, colour = light_bg)
  
  maybe_lm <- function(x) {
    try(lm(y ~ poly(x, input$piecewise_deg), data = x), silent = TRUE)
  }
  
  expansion_to_tibble <- function(x, original, prefix = "term ") {
    cls <- class(x)[1]
    nms <- recipes::names0(ncol(x), prefix)
    colnames(x) <- nms
    x <- as_tibble(x)
    x$variable <- original
    res <- tidyr::pivot_longer(x, cols = c(-variable))
    if (cls != "poly") {
      res <- res[res$value > .Machine$double.eps,]
    }
    res
  } 
  
  mult_poly <- function(dat, degree = 4) {
    rng <- extendrange(dat$x, f = .025)
    grid <- seq(rng[1], rng[2], length.out = 1000)
    grid_df <- tibble(x = grid)
    feat <- poly(grid_df$x, degree)
    res <- expansion_to_tibble(feat, grid_df$x)
    
    # make some random names so that we can plot the features with distinct colors
    rand_names <- lapply(1:degree, function(x) paste0(sample(letters)[1:10], collapse = ""))
    rand_names<- unlist(rand_names)
    rand_names <- tibble(name = unique(res$name), name2 = rand_names)
    res <- 
      dplyr::inner_join(res, rand_names, by = dplyr::join_by(name)) %>% 
      dplyr::select(-name) %>% 
      dplyr::rename(name = name2)
    res
  }
  
  # ------------------------------------------------------------------------------
  
  spline_example <- tibble(y = ames$Sale_Price, x = ames$Latitude)
  rng <- extendrange(ames$Latitude, f = .025)
  grid <- seq(rng[1], rng[2], length.out = 1000)
  grid_df <- tibble(x = grid)
  alphas <- 1 / 4
  line_wd <- 1.0
  
  base_p <-
    spline_example %>%
    ggplot(aes(x = x, y = y)) +
    geom_point(alpha = 3 / 4, pch = 1, cex = 3) +
    labs(x = "Predictor", y = "Outcome") +
    theme_light_bl()
  
  output$spline <- renderPlot({
    
    spline_fit <- lm(y ~ naturalSpline(x, df = input$spline_df), data = spline_example)
    spline_pred <- 
      predict(spline_fit, grid_df, interval = "confidence", level = .90) %>% 
      bind_cols(grid_df)
    
    spline_p <- base_p +
      geom_ribbon(
        data = spline_pred,
        aes(y = NULL, ymin = lwr, ymax = upr),
        alpha = 1 / 15) +
      geom_line(
        data = spline_pred,
        aes(y = fit),
        col = "red",
        linewidth = line_wd)
    
    feature_p <-
      naturalSpline(grid_df$x, df = input$spline_df) %>% 
      expansion_to_tibble(grid_df$x) %>% 
      ggplot(aes(variable, y = value, group = name, col = name)) +
      geom_line(show.legend = FALSE) + # , linewidth = 1, alpha = 1 / 2
      theme_void() +
      theme(
        plot.margin = margin(t = 0, r = 0, b = -20, l = 0),
        panel.background = col_rect,
        plot.background = col_rect,
        legend.background = col_rect,
        legend.key = col_rect
      ) +
      scale_color_viridis(discrete = TRUE, option = "turbo")
    
    p <- (feature_p / spline_p) + plot_layout(heights = c(1, 4))
    
    print(p)
    
  })
  
}

app <- shinyApp(ui = ui, server = server)

Adding splines!

food_rec <- recipe(time_to_delivery ~ ., data = delivery_train) %>% 
  step_dummy(all_factor_predictors()) %>% 
  step_spline_natural(hour, distance, deg_free = 5)

We’ll choose five terms for the two predictors (but we could optimize these).

The new column naming convention is: hour_1 - hour_5.

Adding interactions

food_rec <- recipe(time_to_delivery ~ ., data = delivery_train) %>% 
  step_dummy(all_factor_predictors()) %>% 
  step_spline_natural(hour, distance, deg_free = 5) %>% 
  step_interact(~ starts_with("hour_"):starts_with("day_"))

These new columns are named (by default) to be hour_1_x_day_Tue through hour_5_x_day_Sun.

Question: In a new step, how would you select the interactions?

List of known recipe steps.

Combining a model and a recipe

We can make a new type of object called a ~~pipeline~~ workflow that can be used as one unit.

lm_wflow <- 
  workflow() %>% 
  add_model(linear_reg()) %>% 
  add_recipe(food_rec)

(TMwR)

Combining a model and a recipe

lm_wflow
#> ══ Workflow ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ──────────────────────────────────────────────────────
#> 3 Recipe Steps
#> 
#> • step_dummy()
#> • step_spline_natural()
#> • step_interact()
#> 
#> ── Model ─────────────────────────────────────────────────────────────
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm

Combining a model and a recipe

lm_fit <- fit(lm_wflow, data = delivery_train)

tidy(lm_fit) %>% arrange(term)
#> # A tibble: 74 × 5
#>    term        estimate std.error statistic  p.value
#>    <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#>  1 (Intercept)   12.5       0.834    14.9   8.35e-50
#>  2 day_Fri       -4.62      0.867    -5.33  9.89e- 8
#>  3 day_Sat       -4.59      0.857    -5.35  9.02e- 8
#>  4 day_Sun       -3.12      0.904    -3.45  5.62e- 4
#>  5 day_Thu       -3.54      0.912    -3.88  1.05e- 4
#>  6 day_Tue       -0.509     1.01     -0.505 6.13e- 1
#>  7 day_Wed       -2.83      0.895    -3.17  1.54e- 3
#>  8 distance_1     1.39      0.460     3.03  2.46e- 3
#>  9 distance_2     1.53      0.220     6.94  4.36e-12
#> 10 distance_3     2.67      0.259    10.3   9.43e-25
#> # ℹ 64 more rows

A better path to metrics

val_rs <- validation_set(delivery_split)
val_rs
#> # A tibble: 1 × 2
#>   splits              id        
#>   <list>              <chr>     
#> 1 <split [8008/1000]> validation

lm_res <- 
  lm_wflow %>% 
  fit_resamples(val_rs, control = control_resamples(save_pred = TRUE))

lm_res
#> # Resampling results
#> # Validation Set (0.89/0.11) 
#> # A tibble: 1 × 5
#>   splits              id         .metrics .notes   .predictions
#>   <list>              <chr>      <list>   <list>   <list>      
#> 1 <split [8008/1000]> validation <tibble> <tibble> <tibble>

(TMwR)

Performance

collect_metrics(lm_res)
#> # A tibble: 2 × 6
#>   .metric .estimator  mean     n std_err .config             
#>   <chr>   <chr>      <dbl> <int>   <dbl> <chr>               
#> 1 rmse    standard   2.39      1      NA Preprocessor1_Model1
#> 2 rsq     standard   0.881     1      NA Preprocessor1_Model1

Does it work better?

lm_res %>% 
  collect_predictions() %>% 
  ggplot(aes(.pred, time_to_delivery)) + 
  geom_abline(col = "green", lty = 2) +
  geom_point(alpha = 1 / 3) +
  geom_smooth(se = FALSE) +
  coord_obs_pred()

Not perfect but 👍.

The probably package has better tools to visualize and mitigate calibration issues.

Next Steps

We would

do some exploratory data analyses to figure out better features.
tune the model (e.g., optimize the number of spline terms) (TMwR)
try a different estimation method (Bayesian, robust, etc.)

However…

Let’s try a different model 📏📏📏📏

Rule-based ensembles

Cubist is a rule-based ensemble.

It first creates a regression tree and converts it into a rule

A path to a terminal node

For each rule, it creates a corresponding linear model.

It makes many of these rule sets.

A regression tree

Two example rules

if
   hour <= 14.252
   day in {Fri, Sat}
then
   outcome = -23.039 + 2.85 hour + 1.25 distance + 0.4 item_24
             + 0.4 item_08 + 0.6 item_01 + 0.6 item_10 + 0.5 item_21

if
   hour > 15.828
   distance <= 4.35
   item_10 > 0
   day = Thu
then
   outcome = 11.29956 + 4.24 distance + 14.3 item_01 + 4.3 item_10
             + 2.1 item_02 + 0.03 hour

Fitting a Cubist model

library(rules)

cubist_res <- 
  cubist_rules() %>% 
  fit_resamples(
    time_to_delivery ~ ., 
    resamples = val_rs, 
    control = control_resamples(save_pred = TRUE))

collect_metrics(cubist_res)
#> # A tibble: 2 × 6
#>   .metric .estimator  mean     n std_err .config             
#>   <chr>   <chr>      <dbl> <int>   <dbl> <chr>               
#> 1 rmse    standard   2.18      1      NA Preprocessor1_Model1
#> 2 rsq     standard   0.901     1      NA Preprocessor1_Model1

Does it work better?

cubist_res %>% 
  collect_predictions() %>% 
  ggplot(aes(.pred, time_to_delivery)) + 
  geom_abline(col = "green", lty = 2) +
  geom_point(alpha = 1 / 3) +
  geom_smooth(se = FALSE) +
  coord_obs_pred()

Nice

Moving to the test set

A shortcut

cubist_wflow <- workflow(time_to_delivery ~ ., cubist_rules())

cubist_final_res <- cubist_wflow %>% last_fit(delivery_split)

cubist_final_res
#> # Resampling results
#> # Manual resampling 
#> # A tibble: 1 × 6
#>   splits              id     .metrics .notes   .predictions .workflow 
#>   <list>              <chr>  <list>   <list>   <list>       <list>    
#> 1 <split [8008/1004]> train… <tibble> <tibble> <tibble>     <workflow>

Test set results

collect_metrics(cubist_final_res)
#> # A tibble: 2 × 4
#>   .metric .estimator .estimate .config             
#>   <chr>   <chr>          <dbl> <chr>               
#> 1 rmse    standard       2.08  Preprocessor1_Model1
#> 2 rsq     standard       0.914 Preprocessor1_Model1

Not too bad.

cubist_final_res %>% 
  collect_predictions() %>% 
  ggplot(aes(.pred, time_to_delivery)) + 
  geom_abline(col = "green", lty = 2) +
  geom_point(alpha = 1 / 3) +
  geom_smooth(se = FALSE) +
  coord_obs_pred()

Hmm. Could be better.

Resources

tidyverse: r4ds.hadley.nz

Resources

tidyverse: r4ds.hadley.nz
tidymodels: tmwr.org

Resources

tidyverse: r4ds.hadley.nz
tidymodels: tmwr.org
webpage: tidymodels.org
homepage: workshops.tidymodels.org
aml4td: tidymodels.aml4td.org

Thanks

Thanks for the invitation to speak today!

The tidymodels team: Hannah Frick, Emil Hvitfeldt, and Simon Couch.

Special thanks to the other folks who contributed so much to tidymodels: Davis Vaughan, Julia Silge, Edgar Ruiz, Alison Hill, Desirée De Leon, our previous interns, and the tidyverse team.

Introduction to Machine Learning with tidymodels

👋 Who am I?

👋 Who are we?

👋 Who are you?

What is tidymodels?

BYO Venn Diagram

The Meta-package

Why tidymodels?

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Safety1

Why tidymodels? Safety1

Why tidymodels? Safety

Why tidymodels? Completeness

Why tidymodels? Completeness

Why tidymodels? Completeness

Why tidymodels? Extensibility

Why tidymodels? Extensibility

Why tidymodels? Deployability

Applied example 🍕🍕

Food deliveries

Investigate the data!

Splitting the data

Splitting the data (with validation)

A linear model

A linear model

Does it work?

Measuring performance

Feature engineering

Initializing the recipe

Converting to indicator variables

Splines are the best

Adding splines!

Adding interactions

Combining a model and a recipe

Combining a model and a recipe

Combining a model and a recipe

A better path to metrics

Performance

Does it work better?

Next Steps

Let’s try a different model 📏📏📏📏

Rule-based ensembles

A regression tree

Two example rules

Fitting a Cubist model

Does it work better?

Moving to the test set

A shortcut

Test set results

Resources

Resources

Resources

Resources

Thanks

Why tidymodels? Safety¹

Why tidymodels? Safety¹