शुरू करेंमुफ़्त में शुरू करें

Evaluate the ensemble

So far, so good. But how good exactly? Prove your model evaluation skills by cross-validating your out-of-sample AUC!

The specification boost_spec and the customers_train tibble are still loaded.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Machine Learning with Tree-Based Models in R

पाठ्यक्रम देखें

अभ्यास निर्देश

  • Create five CV folds of your training set and save them as folds.
  • Fit and evaluate a model that predicts still_customer for every fold, using your specification, all predictor variables, and the AUC metric.
  • Collect the metrics of cv_results and check the mean AUC.

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

set.seed(99)

# Create CV folds
folds <- ___

# Fit and evaluate models for all folds
cv_results <- fit_resamples(___,
                            ___,
                            resamples = ___,
                            ___)

# Collect cross-validated metrics
___
कोड संपादित करें और चलाएँ