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.
Este exercício faz parte do curso
Machine Learning in the Tidyverse
Instruções do exercício
- 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.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
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
___