Predicting children in hotel bookings

Application exercise
Modified

September 12, 2024

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/

lr_mod <- logistic_reg() |> 
  set_engine(engine = "glm") |> 
  set_mode("classification")
lr_mod

Your Turn 2

Fill in the blanks.

Use initial_split(), training(), and testing() to:

  1. Split hotels into training and test sets. Save the rsplit!

  2. Extract the training data and fit your classification tree model.

  3. Check the proportions of the test variable in each set.

Keep set.seed(100) at the start of your code.

Hint: Be sure to remove every _ before running the code!

set.seed(100) # Important!
hotels_split <- ________(hotels, prop = 3 / 4)
hotels_train <- ________(hotels_split)
hotels_test <- ________(hotels_split)

# check distribution
count(x = hotels_train, children) |>
  mutate(prop = n / sum(n))
count(x = hotels_test, children) |>
  mutate(prop = n / sum(n))

Your Turn 3

Run the code below. What does it return?

set.seed(100)
hotels_folds <- vfold_cv(data = hotels_train, v = 10)
hotels_folds

Your 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)

tree_preds |> 
  collect_predictions() |> 
  roc_curve(truth = children, .pred_children) |>
  ________()

Add response here.

Acknowledgments