集成学习：随机森林、GBDT、XGBoost.rar

[{"title":"（ 17 个子文件 522KB ） 集成学习：随机森林、GBDT、XGBoost.rar","children":[{"title":"[20180311]_集成学习：随机森林、GBDT、XGBoost","children":[{"title":"xgb.model <span style='color:#111;'> 1.11KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"03_Adaboost案例二：Adaboost API algorithm参数取值比较-checkpoint.ipynb <span style='color:#111;'> 72.91KB </span>","children":null,"spread":false},{"title":"01_随机森林案例一：宫颈癌预测-checkpoint.ipynb <span style='color:#111;'> 134.87KB </span>","children":null,"spread":false},{"title":"04_使用XGBoost算法实现波士顿房价预测-checkpoint.ipynb <span style='color:#111;'> 89.86KB </span>","children":null,"spread":false},{"title":"-1. Bagging&Boosting算法应用在回归模型中-checkpoint.ipynb <span style='color:#111;'> 6.33KB </span>","children":null,"spread":false},{"title":"02_Adaboost案例一：Adaboost分类算法-checkpoint.ipynb <span style='color:#111;'> 54.90KB </span>","children":null,"spread":false}],"spread":true},{"title":"04_使用XGBoost算法实现波士顿房价预测.ipynb <span style='color:#111;'> 89.86KB </span>","children":null,"spread":false},{"title":"xgb.fmap <span style='color:#111;'> 97B </span>","children":null,"spread":false},{"title":"datas","children":[{"title":"boston_housing.data <span style='color:#111;'> 47.93KB </span>","children":null,"spread":false},{"title":"newdata.csv <span style='color:#111;'> 176.32KB </span>","children":null,"spread":false},{"title":"yinzi.csv <span style='color:#111;'> 591B </span>","children":null,"spread":false},{"title":"iris.data <span style='color:#111;'> 4.44KB </span>","children":null,"spread":false},{"title":"risk_factors_cervical_cancer.csv <span style='color:#111;'> 99.67KB </span>","children":null,"spread":false}],"spread":true},{"title":"02_Adaboost案例一：Adaboost分类算法.ipynb <span style='color:#111;'> 54.90KB </span>","children":null,"spread":false},{"title":"-1. Bagging&Boosting算法应用在回归模型中.ipynb <span style='color:#111;'> 6.98KB </span>","children":null,"spread":false},{"title":"01_随机森林案例一：宫颈癌预测.ipynb <span style='color:#111;'> 134.87KB </span>","children":null,"spread":false},{"title":"03_Adaboost案例二：Adaboost API algorithm参数取值比较.ipynb <span style='color:#111;'> 72.91KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]