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.

Diese Übung ist Teil des Kurses

Machine Learning with caret in R

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Anleitung zur Übung

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.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

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

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