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 exercício faz parte do curso
Machine Learning with caret in R
Instruções do exercício
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.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Fit glmnet model: model_glmnet
model_glmnet <- train(
x = churn_x,
y = churn_y,
metric = "ROC",
method = ___,
trControl = ___
)