Tune random forest models

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

This exercise is part of the course

Machine Learning in the Tidyverse

Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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)))

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