AE 05: Predicting hotel price (with numeric engineering!)
Suggested answers
Application exercise
Answers
Setup
reg_metrics <- metric_set(mae, rmse, rsq)
data(hotel_rates)
set.seed(295)
hotel_rates <- hotel_rates |>
sample_n(5000) |>
arrange(arrival_date) |>
select(-arrival_date) |>
mutate(
company = factor(as.character(company)),
country = factor(as.character(country)),
agent = factor(as.character(agent))
)
set.seed(4028)
hotel_split <- initial_split(hotel_rates, strata = avg_price_per_room)
hotel_train <- training(hotel_split)
hotel_test <- testing(hotel_split)set.seed(472)
hotel_folds <- vfold_cv(hotel_train, strata = avg_price_per_room)
hotel_folds# 10-fold cross-validation using stratification
# A tibble: 10 × 2
splits id
<list> <chr>
1 <split [3372/377]> Fold01
2 <split [3373/376]> Fold02
3 <split [3373/376]> Fold03
4 <split [3373/376]> Fold04
5 <split [3373/376]> Fold05
6 <split [3374/375]> Fold06
7 <split [3375/374]> Fold07
8 <split [3376/373]> Fold08
9 <split [3376/373]> Fold09
10 <split [3376/373]> Fold10Adjust for skewness
Your turn: Examine hotel_train and identify a numeric predictor that is skewed. Incorporate an appropriate transformation into the recipe below and estimate a linear regression model using 10-fold cross-validation. How does the model perform with and without the transformation?
hotel_rec <- recipe(avg_price_per_room ~ ., data = hotel_train) |>
step_dummy(all_nominal_predictors()) |>
step_zv(all_predictors())
Tip
Use GGally::ggpairs() to generate bivariate comparisons for all variables in your dataset.
hotel_train |>
select(where(is.numeric)) |>
ggpairs()
# try Yeo-Johnson with all numeric predictors
hotel_yj_rec <- recipe(avg_price_per_room ~ ., data = hotel_train) |>
step_YeoJohnson(all_numeric_predictors()) |>
step_dummy(all_nominal_predictors()) |>
step_zv(all_predictors())
# check the recipe
hotel_yj_rec |>
prep() |>
tidy(number = 1)# A tibble: 7 × 3
terms value id
<chr> <dbl> <chr>
1 lead_time 0.173 YeoJohnson_f7mCz
2 stays_in_weekend_nights 0.00547 YeoJohnson_f7mCz
3 stays_in_week_nights 0.121 YeoJohnson_f7mCz
4 booking_changes -4.16 YeoJohnson_f7mCz
5 total_of_special_requests -0.672 YeoJohnson_f7mCz
6 arrival_date_num -3.21 YeoJohnson_f7mCz
7 historical_adr -0.591 YeoJohnson_f7mCz# original model
set.seed(9)
hotel_lm_wflow <- workflow() |>
add_recipe(hotel_rec) |>
add_model(linear_reg())
hotel_lm_res <- hotel_lm_wflow |>
fit_resamples(hotel_folds,
metrics = reg_metrics,
control = control_resamples(save_pred = TRUE)
)
collect_metrics(hotel_lm_res)# A tibble: 3 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 mae standard 17.1 10 0.176 Preprocessor1_Model1
2 rmse standard 22.8 10 0.296 Preprocessor1_Model1
3 rsq standard 0.881 10 0.00359 Preprocessor1_Model1# model with transforms
set.seed(9)
hotel_lm_skew_wflow <- workflow() |>
add_recipe(hotel_yj_rec) |>
add_model(linear_reg())
hotel_lm_skew_res <- hotel_lm_skew_wflow |>
fit_resamples(hotel_folds,
metrics = reg_metrics,
control = control_resamples(save_pred = TRUE)
)
collect_metrics(hotel_lm_skew_res)# A tibble: 3 × 6
.metric .estimator mean n std_err .config
<chr> <chr> <dbl> <int> <dbl> <chr>
1 mae standard 18.9 10 0.261 Preprocessor1_Model1
2 rmse standard 25.3 10 0.374 Preprocessor1_Model1
3 rsq standard 0.853 10 0.00384 Preprocessor1_Model1Spline functions
Your turn: Implement a natural spline for lead_time and historical_adr. Use grid tuning to determine the optimal value for deg_free. Evaluate the model’s performance.
ns_rec <- recipe(avg_price_per_room ~ ., data = hotel_train) |>
step_ns(lead_time, historical_adr, deg_free = tune()) |>
step_novel(all_nominal_predictors()) |>
step_dummy(all_nominal_predictors())
ns_wf <- workflow() |>
add_recipe(ns_rec) |>
add_model(linear_reg())
ns_res <- ns_wf |>
tune_grid(
hotel_folds,
grid = 10,
metrics = reg_metrics
)autoplot(ns_res)
show_best(ns_res, metric = "mae")# A tibble: 5 × 7
deg_free .metric .estimator mean n std_err .config
<int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 13 mae standard 16.4 10 0.261 Preprocessor9_Model1
2 14 mae standard 16.4 10 0.253 Preprocessor5_Model1
3 11 mae standard 16.4 10 0.251 Preprocessor2_Model1
4 10 mae standard 16.5 10 0.239 Preprocessor6_Model1
5 9 mae standard 16.6 10 0.231 Preprocessor8_Model1MARS model
Your turn: Implement a MARS model. Use grid tuning to determine the optimal value for num_terms and prod_degree. Evaluate the model’s performance.
# recipe
mars_rec <- recipe(avg_price_per_room ~ ., data = hotel_train) |>
step_novel(all_nominal_predictors()) |>
step_dummy(all_nominal_predictors())
# model specification
mars_spec <- mars(mode = "regression", num_terms = tune(), prod_degree = tune())
# workflow
mars_wf <- workflow() |>
add_recipe(mars_rec) |>
add_model(mars_spec)
# space filling grid
set.seed(749)
mars_params <- extract_parameter_set_dials(mars_wf) |>
grid_space_filling(size = 10)
# tune the model
mars_res <- mars_wf |>
tune_grid(
hotel_folds,
grid = mars_params,
metrics = reg_metrics
)autoplot(mars_res)
show_best(mars_res, metric = "mae")# A tibble: 5 × 8
num_terms prod_degree .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 5 2 mae standard 24.8 10 0.294 Preprocessor1_Mo…
2 5 1 mae standard 25.8 10 0.289 Preprocessor1_Mo…
3 4 2 mae standard 26.7 10 0.387 Preprocessor1_Mo…
4 3 2 mae standard 28.1 10 0.377 Preprocessor1_Mo…
5 3 1 mae standard 28.8 10 0.349 Preprocessor1_Mo…
Session information
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14)
os macOS Sonoma 14.6.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-09-18
pandoc 3.3 @ /usr/local/bin/ (via rmarkdown)
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