在 XGboost 模型中绘制 MAE、RMSE

Question

我正在尝试根据 XGboost 模型结果绘制 MAE 和 RMSE。首先，我使用 gridsearchcv 来查找参数然后我拟合模型并设置 eval_metrics 在拟合模型时打印出来：

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)

我得到了这个拟合的正确结果：

[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？

Answer 1

您可以创建一个结果字典，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)

在 XGboost 模型中绘制 MAE、RMSE

Plot MAE, RMSE in XGboost model

python

machine-learning

scikit-learn

grid-search

xgboost