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.

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Machine Learning with caret in R

ดูคอร์ส

คำแนะนำการฝึกหัด

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 the trainControl like you've done before and implement the "ranger" method.

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

# Fit random forest: model_rf
model_rf <- train(
  x = ___, 
  y = ___,
  metric = "ROC",
  method = ___,
  trControl = ___
)

แก้ไขและรันโค้ด