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 ofmtry
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)))