Evaluate model performance
Now that you have both the actual and predicted values of each fold you can compare them to measure performance.
For this regression model, you will measure the Mean Absolute Error (MAE) between these two vectors. This value tells you the average difference between the actual and predicted values.
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.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
library(Metrics)
# Calculate the mean absolute error for each validate fold
cv_eval_lm <- cv_prep_lm %>%
mutate(validate_mae = map2_dbl(___, ___, ~mae(actual = .x, predicted = .y)))
# Print the validate_mae column
cv_eval_lm$___
# Calculate the mean of validate_mae column
___