Fine tune your model
Wow! That was a significant improvement over a regression model. Now let's see if you can further improve this performance by fine tuning your random forest models. To do this you will vary the mtry parameter when building your random forest models on your train data.
The default value of mtry for ranger is the rounded down square root of the total number of features (6). This results in a value of 2.
Questo esercizio fa parte del corso
Machine Learning in the Tidyverse
Istruzioni dell'esercizio
- Use
crossing()to expand the cross validation data for values ofmtryranging from 2 through 5. - Build random forest models for each fold/mtry combination.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Prepare for tuning your cross validation folds by varying mtry
cv_tune <- cv_data %>%
crossing(mtry = ___:___)
# Build a model for each fold & mtry combination
cv_model_tunerf <- cv_tune %>%
mutate(model = map2(.x = ___, .y = ___, ~ranger(formula = life_expectancy~.,
data = .x, mtry = .y,
num.trees = 100, seed = 42)))