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.

本练习是课程的一部分

Machine Learning with caret in R

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练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

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

编辑并运行代码