“rank:pairwise” –通过最小化成对损失对任务进行排序 3.eval_metric——评价指标[default=取决于objective参数的取值] 对于回归问题,默认值是rmse,对于分类问题,默认值是error。选项如下所示: rmse(均方根误差) mae(平均绝对误差) logloss(负对数似然函数值) error(二分类误差,阈值0.5) merror(多分类错误率)...
eval_set=None, # 用于评估的数据集,例如:[(X_train, y_train), (X_test, y_test)] eval_metric=None, # 评估函数,字符串类型,例如:'mlogloss' early_stopping_rounds=None, verbose=True, # 间隔多少次迭代输出一次信息 xgb_model=None ) 1. 2. 3. 4. 5. 6. 7. 8. 9. 预测的方法有两种:...
model.save()# Eval model on testscores = model.evaluate(test_dataset, [regression_metric])assertscores[regression_metric.name] <55 开发者ID:deepchem,项目名称:deepchem,代码行数:30,代码来源:test_generalize.py 示例14: test_xgboost_multitask_regression ▲点赞 5▼ # 需要导入模块: import xgboost [...
xgb = XGBRegressor(learning_rate =0.1, n_estimators=1000, max_depth=5, min_child_weight=1, gamma=0.1, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=21, eval_metric = ['auc','error']) SMOTE = xreg.fit(X_train, y_train) ...
9、eval_metric 10、max_depth 11、colsample_bytree&colsample_bylevel&colsample_bynode 12、min_child_weight 13、scale_pos_weight 14、max_delta_step 15、n_jobs/nthread 16、base_score 17、random_state 18、missing (六)附录 1、求解XGBoost的目标函数/结构分数 2、求解w和T,寻找最佳树结构 3、寻找...
--->1mod.fit(X_train.values, y_train.values) /Users/chriseal/anaconda/lib/python2.7/site-packages/xgboost/sklearn.pycinfit(self, X, y, eval_set, eval_metric, early_stopping_rounds, verbose)249early_stopping_rounds=early_stopping_rounds,250evals_result=evals_result, obj=obj, feval=...
scale_pos_weight =1)# modelfit(model, xgtrain)# del xgtrain# prediction(model, X_train, Y)print"trianing the model"model.fit(x_train, y_train, eval_metric='rmse', eval_set=[(x_eval, y_eval)], verbose=True)delx_train, x_eval, y_train, y_eval# fscore = model.Booster.get_...
model = XGBRegressor(n_estimators=550, learning_rate=0.05, max_depth=8, colsample_bytree=0.7, reg_alpha=1, scale_pos_weight=1, reg_lambda=1.1, n_jobs=6) model.fit(x_train, y_train1, verbose=False, eval_metric=['logloss', 'mae'], eval_set=[(x_train, y_train1), (x_test,...
通过我们使用Unity开发游戏,是在PC/Mac上。而一个游戏通常也会有很多的场景,比如A、B、C、D三个...
[Int]=20 Eval@metricName[String]=areaUnderROC @maxDepth[Int]_auto={"value":"{2,3,5}","initValue":2} 决策树自动调参一直报错 浏览697提问于2019-09-18 1回答 决策树的超参数调优然后在Adaboost中单独使用还是同时产生相同的结果? 、、、 所以,我在这里的困境是,我在一个独立决策树分类器上执行...