Python 在XGboost模型中绘制MAE、RMSE

我试图根据XGboost模型的结果绘制MAE和RMSE。 首先，我使用gridsearchcv查找参数 然后，我拟合模型，并在拟合模型时设置要打印的评估指标：

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
                      )

#Fit model
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)

我获得了此拟合的正确结果：

[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

但是，我尝试访问这些值以创建绘图，但出现以下错误：

myModel.evals_result()

AttributeError: 'GridSearchCV' object has no attribute 'evals_result'

---

如何访问这些值？

您可以创建一个结果dict，然后将其传递给fit

progress = dict()

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

print(progress)