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.

Este ejercicio forma parte del curso

Machine Learning with caret in R

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Instrucciones del ejercicio

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.

Ejercicio interactivo práctico

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

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