随机森林算法OOB_SCORE最佳特征选择 RandomForest算法(有监督学习),可以根据输入数据,选择最佳特征组合,减少特征冗余; 原理:由于随机决策树生成过程采用的Boostrap,所以在一棵树的生成过程并不会使用所有的样本,未使用的样本就叫(Out_of_bag)袋外样本,通过袋外样本,可以评估这个树的准确度,其他子树叶按这个原理评估,最...
cross_val_score(KNN(4),X_dr,y,cv=5).mean() 降维过程中,在选取特征时是有随机性的,我们并没有设置random_state,所以每次结果会有不同。 可以发现,原本785列的特征被我们缩减到23列之后,用KNN跑出了目前位置这个数据集上最好的结果。再进行更细致的调整,我们也许可以将KNN的效果调整到98%以上。PCA为我们...
# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]# 或者: from sklearn.ensemble.RandomForestClassifier importoob_score[as 别名]model = RandomForestClassifier(n_jobs=6)ifargs.CV: parameters = {'n_estimators': [150,175,200],'oob_score': [True,False]}fromsklearn...
When this procedure is repeated, such as when developing a random forest, numerous bootstrap samples and OOB sets are generated. The OOB sets can be combined into a single dataset, however, each sample is only considered out-of-bag for trees that do not include it in their bootstrap sample...
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