我试图从XGboost模型的结果中绘制MAE和RMSE。首先，我使用gridsearchcv来寻找参数，然后我拟合模型，并在拟合模型时设置eval_metrics以便输出结果：

myModel = GridSearchCV(estimator=XGBRegressor(                        learning_rate=0.01,                        n_estimators=500,                        max_depth=5,                        min_child_weight=5,                        gamma=0,                        subsample=0.8,                        colsample_bytree=0.8,                         eval_metric ='mae',                        reg_alpha=0.05                        ),                       param_grid = param_search,                       cv = TimeSeriesSplit(n_splits=5),n_jobs=-1                      )#拟合模型eval_set = [(X_train, y_train), (X_test, y_test)]eval_metric = ["rmse","mae"]history=myModel.fit(X_train, y_train, eval_metric=eval_metric, eval_set=eval_set)

我得到了正确的拟合结果：

[0] validation_0-rmse:7891  validation_0-mae:7791.42    validation_1-rmse:6465.99   validation_1-mae:6465.52[1] validation_0-rmse:7813.98   validation_0-mae:7714.55    validation_1-rmse:6398.87   validation_1-mae:6398.4

然而，当我试图访问这些值以创建图表时，我遇到了以下错误：

myModel.evals_result()AttributeError: 'GridSearchCV' object has no attribute 'evals_result'

我如何访问这些值呢？

回答：

你可以创建一个结果字典，然后将其传递给fit函数

progress = dict()history=myModel.fit(X_train, y_train, evals_result=progress eval_metric=eval_metric, eval_set=eval_set)print(progress)