[Machine Learning] some concept about the CV
Cross-validation VS SSE
CV is not designed to improve the fit on the training data, but it won't necessarily make it worse either. Cross-validation cannot guarantee improving the SSE on unseen data, although it often helps.
Cross-validation VS best prediction
The purpose of cross-validation is to pick the tree that will perform the best on a test set. So we would expect the model we made with the "best" cp to perform best on a test set.
complexity of the model
Trying different combinations of variables in linear regression controls the complexity of the model. This is similar to trying different numbers of splits in a tree, which is also controlling the complexity of the model.
posted on 2017-03-17 21:52 xiaojin693 阅读(111) 评论(0) 编辑 收藏 举报