Preparing for evaluation
In order to measure the validate performance of your models you need compare the predicted values of life_expectancy for the observations from validate set to the actual values recorded. Here you will prepare both of these vectors for each partition.
Este exercício faz parte do curso
Machine Learning in the Tidyverse
Instruções do exercício
- Extract the actual
life_expectancyfrom the validate data frames and store these in the columnvalidate_actual. - Predict the
life_expectancyfor each validate partition using themap2()andpredict()functions in the columnvalidate_predicted.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
cv_prep_lm <- cv_models_lm %>%
mutate(
# Extract the recorded life expectancy for the records in the validate data frames
validate_actual = map(validate, ~.x$___),
# Predict life expectancy for each validate set using its corresponding model
validate_predicted = map2(.x = model, .y = validate, ~___(.x, .y))
)