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.
Questo esercizio fa parte del corso
Machine Learning with caret in R
Istruzioni dell'esercizio
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.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Fit glmnet model: model_glmnet
model_glmnet <- train(
x = churn_x,
y = churn_y,
metric = "ROC",
method = ___,
trControl = ___
)