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Out-of-sample performance

In-sample performance provides insights about how well a model captures the data it is modeling. For predictive models, it's also important to check model performance on new, unseen data, the out-of-sample performance.

In this exercise, you will check the test set predictions of your model using MAE (mean absolute error).

Pre-loaded in your workspace again is the model that you built and used in the last exercises.

Cet exercice fait partie du cours

Machine Learning with Tree-Based Models in R

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Instructions

  • Use model to predict the out-of-sample final_grade and add your predictions to chocolate_test using bind_cols().
  • Calculate the mean absolute error using a yardstick function.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Predict ratings on test set and add true grades
test_enriched <- predict(__, new_data = ___) %>%
    bind_cols(___)
    
# Compute the mean absolute error using one single function
___(___,
    ___,
    ___)
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