What is Out-of-Bag Error? The Out-of-Bag (OOB) error is a method of measuring the prediction error of random forests, bagged decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). It provides an accurate estimate of the model performance without the need...
最终, 假设 类别j 是当记录n是oob时候，获得投票最多的类别，j被错误分类除以总记录数n，就是 oob error estimate. 这在很多测试中被证明是无偏的[2].Out-of-bag 估计的泛化错误率是 out-of-bag classifier 在训练集上的错误率。那么它为什么重要? Breiman [1996b]在对 bagged 分类器的错误率...
The number of variables sampled, m-try, has the largest impact on the true prediction error. It is often claimed that the out-of-bag error (OOB) is an unbiased estimate of the true prediction error. However, for the case where n p, with the default arguments, the out-of-bag (OOB)...
最终, 假设 类别 j 是当记录 n 是 oob 时候,获得投票最多 的类别,j 被错误分类除以总记录数 n,就是 oob error estimate. 这 在很多测试中被证明是无偏的[2]. Out-of-bag 估计的泛化错误率是 out-of-bag classifier 在训练集 上的错误率。 那么它为什么重要? Breiman [1996b]在对 bagged 分类器的...