Evaluate a random forest model

Similar to the linear regression model, you will use the MAE metric to evaluate the performance of the random forest model.

Cet exercice fait partie du cours

<cours>Machine Learning in the Tidyverse</cours>

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Instructions de l’exercice

Calculate the MAE by comparing the actual with the predicted values for the validate data and assign it to the validate_mae column.
Print the validate_mae column (note how they vary).
Calculate the mean of this column.

Note: The actual values of the validate fold (validate_actual) has already been added to your cv_data data frame.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

library(ranger)

# Calculate validate MAE for each fold
cv_eval_rf <- cv_prep_rf %>% 
  mutate(validate_mae = map2_dbl(___, ___, ~mae(actual = .x, predicted = .y)))

# Print the validate_mae column
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# Calculate the mean of validate_mae column
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