机器学习sklearn(89):算法实例(46)分类(25)XGBoost(三)梯度提升树(二)有放回随机抽样:重要参数subsample/迭代决策树:重要参数eta 1 有放回随机抽样:重要参数subsample axisx = np.linspace(0,1,20) rs=[]foriinaxisx: reg= XGBR(n_estimators=180,subsample=i,random_state=420) rs.append(CVS(reg,Xtra...
在XGBoost中,Subsample参数也是控制样本随机采样比例的参数。不同的是,在XGBoost中,Subsample参数除了可以控制样本采样比例之外,还可以控制列采样比例。这是因为XGBoost使用的是基于梯度提升决策树的算法,与随机森林算法不同,每棵决策树都是在上一棵决策树的残差基础上构建的,因此,每棵树的正确性对于整个模型的性能至关...
XGBoost even has 3 different ones: colsample_bytree, colsample_bylevel, colsample_bynode. The last one is for split-base subsampling. I propose we go with colsample_bynode, i.e. per-split subsampling. 👍 1 lorentzenchr changed the title [Feature Request] Add subsample and max_features ...