What is the point of making a train/test split for binary classification problems?
本练习是课程的一部分
通过我们的互动练习之一，将理论转化为实践
In the first chapter of this course, you'll fit regression models with <code>train()</code> and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
In this chapter, you'll fit classification models with <code>train()</code> and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
当前练习
In this chapter, you will use the <code>train()</code> function to tweak model parameters through cross-validation and grid search.
In this chapter, you will practice using <code>train()</code> to preprocess data before fitting models, improving your ability to making accurate predictions.
In the final chapter of this course, you'll learn how to use <code>resamples()</code> to compare multiple models and select (or ensemble) the best one(s).
