Tune random forest models

Now that you have a working logistic regression model you will prepare a random forest model to compare it with.

Este exercício faz parte do curso

Machine Learning in the Tidyverse

Instruções do exercício

Use crossing() to expand the cross-validation data for values of mtry using the values of 2, 4, 8, and 16.
Build random forest models for each fold/mtry combination.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

library(ranger)

# Prepare for tuning your cross validation folds by varying mtry
cv_tune <- cv_data %>%
  crossing(mtry = c(___)) 

# Build a cross validation model for each fold & mtry combination
cv_models_rf <- cv_tune %>% 
  mutate(model = map2(___, ___, ~ranger(formula = Attrition~., 
                                           data = .x, mtry = .y,
                                           num.trees = 100, seed = 42)))

Editar e executar o código