Exercise

# 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.

Instructions

**100 XP**

- 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"]]`

.