With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, \ depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15) clf.fit(train,y_train, cat_features= cat_features_index) auc(clf, train, test) 结语 为了评估模型,我们应该同时考虑模型的速度...
With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC", depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15) clf.fit(train,y_train) auc(clf, train, test) With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, \\ depth...
With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC", depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15) clf.fit(train,y_train) auc(clf, train, test) With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, depth=10...
model2 = lgb.train(params, d_train) auc2(model2, train, test) # With Catgeorical Features cate_features_name = ["MONTH","DAY","DAY_OF_WEEK","AIRLINE","DESTINATION_AIRPORT", "ORIGIN_AIRPORT"] model2 = lgb.train(params, d_train, categorical_feature = cate_features_name) auc2(model...
With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, depth=10, iterations=500, l2_leaf_reg=9, learning_rate=0.15) clf.fit(train,y_train, cat_features= cat_features_index) auc(clf, train, test) ...
# Without Categorical Featuresmodel2 = lgb.train(params, d_train)auc2(model2, train, test) # With Catgeorical Featurescate_features_name = ["MONTH","DAY","DAY_OF_WEEK","AIRLINE","DESTINATION_AIRPORT","ORIGIN_AIRPORT"]m...
With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15) clf.fit(train,y_train, cat_features= cat_features_index) auc(clf, train, test) ...
With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC", depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15) clf.fit(train,y_train) auc(clf, train, test) With Categorical features clf = cb.CatBoostClassifier(eval_metric="AUC",one_hot_max_size=31, ...
支持类别特征(Categorical Feature) Cache命中率优化 基于直方图的稀疏特征优化多线程优化。 3.Histogram算法 直方图算法的基本思想是先把连续的浮点特征值离散化成k个整数(其实又是分桶的思想,而这些桶称为bin,比如[0,0.1)→0, [0.1,0.3)→1),同时构造一个宽度为k的直方图。 在遍历数据的时候,根据离散化后的值...
params= {"max_depth": 3,"learning_rate": 0.1,"num_leaves": 900,"n_estimators": 20}#Without Categorical Featuresmodel2 =lgb.train(params, d_train)print(auc2(model2, train, test))#With Catgeorical Featurescate_features_name = ["MONTH","DAY","DAY_OF_WEEK","AIRLINE","DESTINATION_AIR...