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.
Questo esercizio fa parte del corso
Machine Learning in the Tidyverse
Istruzioni dell'esercizio
- Calculate the MAE by comparing the actual with the predicted values for the validate data and assign it to the
validate_maecolumn. - Print the
validate_maecolumn (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.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
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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