Random forest with custom trainControl
Another one of my favorite models is the random forest, which combines an ensemble of non-linear decision trees into a highly flexible (and usually quite accurate) model.
Rather than using the classic randomForest package, you'll be using the ranger package, which is a re-implementation of randomForest that produces almost the exact same results, but is faster, more stable, and uses less memory. I highly recommend it as a starting point for random forest modeling in R.
Questo esercizio fa parte del corso
Machine Learning with caret in R
Istruzioni dell'esercizio
churn_x and churn_y are loaded in your workspace.
- Fit a random forest model to the churn dataset. Be sure to use
myControlas thetrainControllike you've done before and implement the"ranger"method.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Fit random forest: model_rf
model_rf <- train(
x = ___,
y = ___,
metric = "ROC",
method = ___,
trControl = ___
)