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...
oob_score : bool (default=False) Whether to use out-of-bag samples to estimate the generalization accuracy. 中文叫‘袋外误差’,可以看出这个参数的意思是:使用oob来衡量test error. 关于oob的解释,stackoverflow上有比较全面的解释:OOB的解释说下自己的理解: RF需要从原始的特征集中随机sampling,然后去分裂...
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)...
构建随机森林(randomforest,RF)模型,并依据袋外错误率(out-of-bagerrorrate,OOB)对随机森林模型的估计器(决策树)数量和单一决策树最大特征的2个参数进行优化... 孙永,刘楠,李智慧,... - 《食品安全质量检测学报》 被引量: 0发表: 2019年 加载更多 来源...
Estimate Out-Of-Bag Error Copy Code Copy Command Load Fisher's iris data set. Get load fisheriris Grow a bag of 100 classification trees. Get ens = fitcensemble(meas,species,'Method','Bag'); Estimate the out-of-bag classification error. Get L = oobLoss(ens) L = 0.0400 Input...
Estimate predictor importance measures by permuting out-of-bag observations. Compare the estimates using a bar graph. imp = oobPermutedPredictorImportance(Mdl); figure; bar(imp); title('Out-of-Bag Permuted Predictor Importance Estimates'); ylabel('Estimates'); xlabel('Predictors'); h = gca; ...
The study of error estimates for bagged classifiers gives empirical evidence to show that the out-of-bag estimate is as accurate as using a test set of the same size as the training set. Therefore, using the out-of-bag error estimate removes the need for a set-aside test set. ...
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...
Estimate Out-Of-Bag Error Copy Code Copy Command Load Fisher's iris data set. Get load fisheriris Grow a bag of 100 classification trees. Get ens = fitcensemble(meas,species,'Method','Bag'); Estimate the out-of-bag classification error. Get L = oobLoss(ens) L = 0.0400 Input...