Fit the baseline model

Now that you have a reusable trainControl object called myControl, you can start fitting different predictive models to your churn dataset and evaluate their predictive accuracy.

You'll start with one of my favorite models, glmnet, which penalizes linear and logistic regression models on the size and number of coefficients to help prevent overfitting.

This exercise is part of the course

Machine Learning with caret in R

View Course

Exercise instructions

Fit a glmnet model to the churn dataset called model_glmnet. Make sure to use myControl, which you created in the first exercise and is available in your workspace, as the trainControl object.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Fit glmnet model: model_glmnet
model_glmnet <- train(
  x = churn_x, 
  y = churn_y,
  metric = "ROC",
  method = ___,
  trControl = ___
)

Edit and Run Code