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: 2 × 6
  .metric  .estimator  mean     n std_err .config             
  <chr>    <chr>      <dbl> <int>   <dbl> <chr>               
1 accuracy binary     0.739    10 0.00192 Preprocessor1_Model1
2 roc_auc  binary     0.830    10 0.00352 Preprocessor1_Model1

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: 2 × 6
  .metric  .estimator  mean     n std_err .config             
  <chr>    <chr>      <dbl> <int>   <dbl> <chr>               
1 accuracy binary     0.828    10 0.00215 Preprocessor1_Model1
2 roc_auc  binary     0.873    10 0.00209 Preprocessor1_Model1

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 4.3.1 (2023-06-16)
 os       macOS Ventura 13.5.2
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2023-11-08
 pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
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 [1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library

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