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.
Cet exercice fait partie du cours
Machine Learning with caret in R
Instructions
churn_x
and churn_y
are loaded in your workspace.
- Fit a random forest model to the churn dataset. Be sure to use
myControl
as thetrainControl
like you've done before and implement the"ranger"
method.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Fit random forest: model_rf
model_rf <- train(
x = ___,
y = ___,
metric = "ROC",
method = ___,
trControl = ___
)