AE 15: Building better training data to predict children in hotel bookings

Your Turn 1

Unscramble! You have all the steps from our knn_rec- your challenge is to unscramble them into the right order!

Save the result as knn_rec

step_normalize(all_numeric())

recipe(children ~ ., data = hotels)

step_rm(arrival_date)

step_date(arrival_date)

step_downsample(children)

step_holiday(arrival_date, holidays = holidays)

step_dummy(all_nominal_predictors())

step_zv(all_predictors())

Answer:

knn_rec <- recipe(children ~ ., data = hotels) |>
  step_date(arrival_date) |>
  step_holiday(arrival_date, holidays = holidays) |>
  step_rm(arrival_date) |>
  step_dummy(all_nominal_predictors()) |>
  step_zv(all_predictors()) |>
  step_normalize(all_numeric()) |>
  step_downsample(children)
knn_rec

── Recipe ──────────────────────────────────────────────────────────────────────

── Inputs

Number of variables by role

outcome:    1
predictor: 21

── Operations

• Date features from: arrival_date

• Holiday features from: arrival_date

• Variables removed: arrival_date

• Dummy variables from: all_nominal_predictors()

• Zero variance filter on: all_predictors()

• Centering and scaling for: all_numeric()

• Down-sampling based on: children

Your Turn 2

Fill in the blanks to make a workflow that combines knn_rec and with knn_mod.

knn_wf <- ______ |>
  ______(knn_rec) |>
  ______(knn_mod)
knn_wf

Answer:

knn_wf <- workflow() |>
  add_recipe(knn_rec) |>
  add_model(knn_mod)
knn_wf

══ Workflow ════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: nearest_neighbor()

── Preprocessor ────────────────────────────────────────────────────────────────
7 Recipe Steps

• step_date()
• step_holiday()
• step_rm()
• step_dummy()
• step_zv()
• step_normalize()
• step_downsample()

── Model ───────────────────────────────────────────────────────────────────────
K-Nearest Neighbor Model Specification (classification)

Computational engine: kknn

Your Turn 3

Edit the code chunk below to fit the entire knn_wflow instead of just knn_mod.

set.seed(100)
knn_mod |>
  fit_resamples(
    children ~ .,
    resamples = hotels_folds,
    # print progress of model fitting
    control = control_resamples(verbose = TRUE)
  ) |>
  collect_metrics()

Answer:

set.seed(100)
knn_wf |>
  fit_resamples(
    resamples = hotels_folds,
    control = control_resamples(verbose = TRUE)
  ) |>
  collect_metrics()

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# A tibble: 3 × 6
  .metric     .estimator  mean     n std_err .config        
  <chr>       <chr>      <dbl> <int>   <dbl> <chr>          
1 accuracy    binary     0.744    10 0.00186 pre0_mod0_post0
2 brier_class binary     0.171    10 0.00134 pre0_mod0_post0
3 roc_auc     binary     0.837    10 0.00370 pre0_mod0_post0

Your Turn 4

Turns out, the same knn_rec recipe can also be used to fit a penalized logistic regression model using the lasso. Let’s try it out!

plr_mod <- logistic_reg(penalty = .01, mixture = 1) |>
  set_engine("glmnet") |>
  set_mode("classification")

plr_mod |>
  translate()

Logistic Regression Model Specification (classification)

Main Arguments:
  penalty = 0.01
  mixture = 1

Computational engine: glmnet 

Model fit template:
glmnet::glmnet(x = missing_arg(), y = missing_arg(), weights = missing_arg(), 
    alpha = 1, family = "binomial")

Answer:

glmnet_wf <- knn_wf |>
  update_model(plr_mod)

glmnet_wf |>
  fit_resamples(resamples = hotels_folds) |>
  collect_metrics()

# A tibble: 3 × 6
  .metric     .estimator  mean     n  std_err .config        
  <chr>       <chr>      <dbl> <int>    <dbl> <chr>          
1 accuracy    binary     0.826    10 0.00213  pre0_mod0_post0
2 brier_class binary     0.139    10 0.000925 pre0_mod0_post0
3 roc_auc     binary     0.874    10 0.00213  pre0_mod0_post0

Acknowledgments

Materials derived from Tidymodels, Virtually: An Introduction to Machine Learning with Tidymodels by Allison Hill.
Dataset and some modeling steps derived from A predictive modeling case study and licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA) License.

Session information

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.1 (2025-06-13)
 os       macOS Tahoe 26.0.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2025-10-29
 pandoc   3.4 @ /usr/local/bin/ (via rmarkdown)
 quarto   1.8.24 @ /usr/local/bin/quarto

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