y, # array, Series 类型 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. ...
) reg.fit(X_train, y_train,eval_set = [(X_train, y_train), (X_test, y_test)], eval_metric ='mae') result = reg.evals_result_ 1. 2. 3. 4. 5. 6. 7. 8. 9.
--->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=...
model__eval_set=[(X_valid, y_valid)])andthe errorisValueError: DataFrame.dtypesfordata must beint,floatorbool. Didnotexpect the data typesinfields MSZoning, Street, Alley, LotShape, LandContour, Utilities, LotConfig, LandSlope, Condition1, Condition2, BldgType, HouseStyle, RoofStyle, Roof...
可以尝试编辑MultiOutputRegressor对象的fit方法，如下所示：
管道是并发安全的队列，用于在Go的轻量级线程(Go协程)之间安全地传递消息。总的来讲，这些原语是Go语言...
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_...
eval_metric=xgb_metric, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=self.verbose)# Since test size is 20%, when retrain model to whole data, expect# n_estimator increased to 1/0.8 = 1.25 time.estimated_best_round = np.round(self.model_instance.best_ntree_limit *1....
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,...
“rank:pairwise” –set XGBoost to do ranking task by minimizing the pairwise loss base_score [ default=0.5 ] eval_metric [ default according to objective ] evaluation metrics for validation data, a default metric will be assigned according to objective( rmse for regression, and error for cla...