tree_mod <- decision_tree(engine = "rpart") |>
set_mode("classification")
tree_wf <- workflow() |>
add_formula(children ~ .) |>
add_model(tree_mod)Tune better models to predict children in hotel bookings
Suggested answers
Application exercise
Answers
Your Turn 1
Fill in the blanks to return the accuracy and ROC AUC for this model using 10-fold cross-validation.
Fill in the blanks to return the accuracy and ROC AUC for this model using 10-fold cross-validation.
set.seed(100)
______ |>
______(resamples = hotels_folds) |>
______Answer:
set.seed(100)
tree_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 0.773 10 0.00567 Preprocessor1_Model1
2 brier_class binary 0.158 10 0.00322 Preprocessor1_Model1
3 roc_auc binary 0.832 10 0.00672 Preprocessor1_Model1Your Turn 2
Create a new parsnip model called rf_mod, which will learn an ensemble of classification trees from our training data using the ranger package. Update your tree_wf with this new model.
Fit your workflow with 10-fold cross-validation and compare the ROC AUC of the random forest to your single decision tree model — which predicts the test set better?
Hint: you’ll need https://www.tidymodels.org/find/parsnip/
# model
rf_mod <- _____ |>
_____("ranger") |>
_____("classification")
# workflow
rf_wf <- tree_wf |>
update_model(_____)
# fit with cross-validation
set.seed(100)
_____ |>
fit_resamples(resamples = hotels_folds) |>
collect_metrics()Answer:
# model
rf_mod <- rand_forest(engine = "ranger") |>
set_mode("classification")
# workflow
rf_wf <- tree_wf |>
update_model(rf_mod)
# fit with cross-validation
set.seed(100)
rf_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 0.829 10 0.00332 Preprocessor1_Model1
2 brier_class binary 0.123 10 0.00176 Preprocessor1_Model1
3 roc_auc binary 0.912 10 0.00320 Preprocessor1_Model1Your Turn 3
Challenge: Fit 3 more random forest models, each using 5, 12, and 21 variables at each split. Update your rf_wf with each new model. Which value maximizes the area under the ROC curve?
rf5_mod <- rf_mod |>
set_args(mtry = 5)
rf12_mod <- rf_mod |>
set_args(mtry = 12)
rf21_mod <- rf_mod |>
set_args(mtry = 21) Do this for each model above:
_____ <- rf_wf |>
update_model(_____)
set.seed(100)
_____ |>
fit_resamples(resamples = hotels_folds) |>
collect_metrics()Answer:
# 5
rf5_wf <- rf_wf |>
update_model(rf5_mod)
set.seed(100)
rf5_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 0.829 10 0.00376 Preprocessor1_Model1
2 brier_class binary 0.122 10 0.00176 Preprocessor1_Model1
3 roc_auc binary 0.912 10 0.00305 Preprocessor1_Model1# 12
rf12_wf <- rf_wf |>
update_model(rf12_mod)
set.seed(100)
rf12_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 0.831 10 0.00414 Preprocessor1_Model1
2 brier_class binary 0.123 10 0.00239 Preprocessor1_Model1
3 roc_auc binary 0.908 10 0.00418 Preprocessor1_Model1# 21
rf21_wf <- rf_wf |>
update_model(rf21_mod)
set.seed(100)
rf21_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 0.827 10 0.00382 Preprocessor1_Model1
2 brier_class binary 0.125 10 0.00256 Preprocessor1_Model1
3 roc_auc binary 0.905 10 0.00438 Preprocessor1_Model1Your Turn 4
Edit the random forest model to tune the mtry and min_n hyper-parameters; call the new model spec rf_tuner.
Update your workflow to use the tuned model.
Then use tune_grid() to find the best combination of hyper-parameters to maximize roc_auc; let tune set up the grid for you.
How does it compare to the average ROC AUC across folds from fit_resamples()?
rf_mod <- rand_forest(engine = "ranger") |>
set_mode("classification")
rf_wf <- workflow() |>
add_formula(children ~ .) |>
add_model(rf_mod)
set.seed(100) # Important!
rf_results <- rf_wf |>
fit_resamples(resamples = hotels_folds,
metrics = metric_set(roc_auc),
# change me to control_grid() with tune_grid
control = control_resamples(save_workflow = TRUE))
rf_results |>
collect_metrics()# A tibble: 1 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 roc_auc binary 0.912 10 0.00320 Preprocessor1_Model1Answer:
rf_tuner <- rand_forest(
engine = "ranger",
mtry = tune(),
min_n = tune()
) |>
set_mode("classification")
rf_wf <- rf_wf |>
update_model(rf_tuner)
set.seed(100) # Important!
rf_results <- rf_wf |>
tune_grid(resamples = hotels_folds,
control = control_grid(save_workflow = TRUE))i Creating pre-processing data to finalize unknown parameter: mtryYour Turn 5
Use fit_best() to take the best combination of hyper-parameters from rf_results and use them to predict the test set.
How does our actual test ROC AUC compare to our cross-validated estimate?
hotels_best <- fit_best(rf_results)
# cross validated ROC AUC
rf_results |>
show_best(metric = "roc_auc", n = 5)# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 3 15 roc_auc binary 0.910 10 0.00283 Preprocessor1_Model07
2 8 20 roc_auc binary 0.909 10 0.00376 Preprocessor1_Model10
3 7 36 roc_auc binary 0.908 10 0.00372 Preprocessor1_Model02
4 9 28 roc_auc binary 0.907 10 0.00381 Preprocessor1_Model01
5 12 21 roc_auc binary 0.907 10 0.00430 Preprocessor1_Model03# test set ROC AUC
bind_cols(
hotels_test,
predict(hotels_best, new_data = hotels_test, type = "prob")
) |>
roc_auc(truth = children, .pred_children)# A tibble: 1 × 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 roc_auc binary 0.913# test set ROC curve
bind_cols(
hotels_test,
predict(hotels_best, new_data = hotels_test, type = "prob")
) |>
roc_curve(truth = children, .pred_children) |>
autoplot()
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 4.3.2 (2023-10-31)
os macOS Ventura 13.6.6
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
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tz America/New_York
date 2024-04-25
pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
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