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.

This exercise is part of the course

Machine Learning in the Tidyverse

View Course

Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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
___

# Calculate the mean of validate_mae column
___

Edit and Run Code