xgb_classifier = xgb.XGBClassifier(n_estimators=20,\ max_depth=4, \ learning_rate=0.1, \ subsample=0.7, \ colsample_bytree=0.7, \ eval_metric='error') # Dataframe格式数据拟合模型 xgb_classifier.fit(train[feature_columns], train[target_column]) # 使...
max_leaves [default=0]:设置叶节点的最大数量,仅仅和当row_policy=lossguide才需要被设置; max_bin, [default=256]:仅仅tree_method=hist时,该方法需要去设置。bucket连续特征的最大离散bins数量; 1.3 学习任务参数(Learning Task Parameters) objective [default=reg:linear] reg:line...
示例7: _build_classifier ▲点赞 6▼ # 需要导入模块: import xgboost [as 别名]# 或者: from xgboost importXGBClassifier[as 别名]def_build_classifier(self, n_estimators, min_child_weight, max_depth, gamma, subsample, colsample_bytree, num_class):assertnum_class >=2ifnum_class ==2: clf =...
xgb_classifier.fit(train[feature_columns], train[target_column]) # 使用模型预测 preds = xgb_classifier.predict(test[feature_columns]) # 判断准确率 print('错误类为%f'%((preds!=test[target_column]).sum()/float(test_y.shape[0]))) # 模型存储 joblib.dump(xgb_classifier,'./model/0003.mode...
Let’s use the cv function of XGBoost classifier to do the job again.xgb4 = XGBClassifier( learning_rate =0.01, n_estimators=5000, max_depth=4, min_child_weight=6, gamma=0, subsample=0.8, colsample_bytree=0.8, reg_alpha=0.005, objective= 'binary:logistic', nthread=4, scale_pos_...
初始化模型xgb_classifier = xgb.XGBClassifier(n_estimators=20, max_depth=4, learning_rate=0.1, subsample=0.7, colsample_bytree=0.7, eval_metric='error')# Dataframe格式数据拟合模型xgb_classifier.fit(train[feature_columns], train[target_column])# 使用模型预测preds = xgb_classifier.predict(test[...
# Train LCEClassifier with default parameters clf = LCEClassifier(n_jobs=-1, random_state=123) clf.fit(X_train, y_train) # Make prediction and generate classification report y_pred = clf.predict(X_test) print(classification_report(y_test, y_pred)) ...
(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks= # pylint: disable = attribute-defined-outside-init,arguments-differ None): """ Fit gradient boosting classifier Parameters -...
fit(train[feature_columns], train[target_column]) # 使用模型预测 preds = xgb_classifier.predict(test[feature_columns]) # 判断准确率 print('错误类为%f' %((preds!=test[target_column]).sum()/float(test_y.shape[0]))) # 模型存储 joblib.dump(xgb_classifier, './model/0003.model') 1....
xgb_classifier = xgb.XGBClassifier(n_estimators=20,max_depth=4,learning_rate=0.1, subsample=0.7, colsample_bytree=0.7) # 拟合模型 xgb_classifier.fit(train_X, train_y) # 使用模型预测 preds = xgb_classifier.predict(test_X) # 判断准确率 ...