lr_mod <- logistic_reg() |>
set_engine(engine = "glm") |>
set_mode("classification")
lr_modLogistic Regression Model Specification (classification)
Computational engine: glm AE 14: Predicting children in hotel bookings
Suggested answers
Application exercise
Answers
Your Turn 1
Run the chunk below and look at the output. Then, copy/paste the code and edit to create:
a decision tree model for classification
that uses the {C5.0} engine.
Save it as tree_mod and look at the object. What is different about the output?
Hint: you’ll need https://www.tidymodels.org/find/parsnip/
tree_mod <- decision_tree() |>
set_engine(engine = "C5.0") |>
set_mode("classification")
tree_modDecision Tree Model Specification (classification)
Computational engine: C5.0 Your Turn 2
Fill in the blanks.
Use initial_split(), training(), and testing() to:
Split hotels into training and test sets. Save the
rsplit!Extract the training data and fit your classification tree model.
Check the proportions of the
testvariable in each set.
Keep set.seed(100) at the start of your code.
Hint: Be sure to remove every _ before running the code!
# A tibble: 2 × 3
children n prop
<fct> <int> <dbl>
1 children 1503 0.501
2 none 1497 0.499count(x = hotels_test, children) |>
mutate(prop = n / sum(n))# A tibble: 2 × 3
children n prop
<fct> <int> <dbl>
1 children 497 0.497
2 none 503 0.503Your Turn 3
Run the code below. What does it return?
set.seed(100)
hotels_folds <- vfold_cv(data = hotels_train, v = 10)
hotels_folds# 10-fold cross-validation
# A tibble: 10 × 2
splits id
<list> <chr>
1 <split [2700/300]> Fold01
2 <split [2700/300]> Fold02
3 <split [2700/300]> Fold03
4 <split [2700/300]> Fold04
5 <split [2700/300]> Fold05
6 <split [2700/300]> Fold06
7 <split [2700/300]> Fold07
8 <split [2700/300]> Fold08
9 <split [2700/300]> Fold09
10 <split [2700/300]> Fold10Your Turn 4
Add a autoplot() to visualize the ROC AUC. How well does the model perform?
tree_preds <- tree_mod |>
fit_resamples(
children ~ average_daily_rate + stays_in_weekend_nights,
resamples = hotels_folds,
control = control_resamples(save_pred = TRUE)
)
tree_preds |>
collect_predictions() |>
roc_auc(truth = children, .pred_children)# A tibble: 1 × 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 roc_auc binary 0.670tree_preds |>
collect_predictions() |>
roc_curve(truth = children, .pred_children) |>
autoplot()
It’s moderately successful. Better than \(0.5\), but still has a lot of room for improvement.
Acknowledgments
- Materials derived from Tidymodels, Virtually by Allison Hill and licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA) License.
- 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.
NoteSession 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
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tz America/New_York
date 2025-10-27
pandoc 3.4 @ /usr/local/bin/ (via rmarkdown)
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