【机器学习笔记】第二章：模型评估与选择

2.1 经验误差与过拟合

1. error rate/accuracy

2. error: training error/empirical error, generalization error

3. overfitting and underfitting

2.2 评估方法

1. hold out. 直接将数据集$D$划分为两个互斥的集合 $D = S \cap T, S \cap T = \varnothing$ , stratified sampling:分层采样

2. cross validation.

3. bootstrapping

【机器学习笔记】第二章：模型评估与选择

原文：http://www.cnblogs.com/vincentcheng/p/6921388.html