Fit glmnet with custom trainControl

Now that you have a custom trainControl object, fit a glmnet model to the "don't overfit" dataset. Recall from the video that glmnet is an extension of the generalized linear regression model (or glm) that places constraints on the magnitude of the coefficients to prevent overfitting. This is more commonly known as "penalized" regression modeling and is a very useful technique on datasets with many predictors and few values.

glmnet is capable of fitting two different kinds of penalized models, controlled by the alpha parameter:

Ridge regression (or alpha = 0)
Lasso regression (or alpha = 1)

You'll now fit a glmnet model to the "don't overfit" dataset using the defaults provided by the caret package.

This exercise is part of the course

Machine Learning with caret in R

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Exercise instructions

Train a glmnet model called model on the overfit data. Use the custom trainControl from the previous exercise (myControl). The variable y is the response variable and all other variables are explanatory variables.
Print the model to the console.
Use the max() function to find the maximum of the ROC statistic contained somewhere in model[["results"]].

Hands-on interactive exercise

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

# Fit glmnet model: model
model <- train(
  ___, 
  ___,
  method = "glmnet",
  trControl = ___
)

# Print model to console


# Print maximum ROC statistic

Edit and Run Code