在 Jupyter Notebook 中是否可以查看 GridSearchCV 的进度？我正在运行以下 Python 脚本：

param_grid = {'learning_rate': [0.05, 0.10, 0.15, 0.20, 0.25, 0.30] ,'max_depth'        : [3, 4, 5, 6, 8, 10, 12, 15],'min_child_weight' : [1, 3, 5, 7],'gamma'            : [0.0, 0.1, 0.2 , 0.3, 0.4],'colsample_bytree' : [0.3, 0.4, 0.5 , 0.7],'verbose'          : [100] }xgboost_reg = XGBRegressor()grid_search = GridSearchCV(xgboost_reg, param_grid, cv=5, scoring='neg_mean_squared_error', return_train_score=True)grid_search.fit(my_data, my_labels, verbose=False)

我在单元格输出中只能看到一些警告信息。

回答：

您需要使用 verbose 参数：

grid_search = GridSearchCV(xgboost_reg, param_grid, cv=5, scoring='neg_mean_squared_error', return_train_score=True, verbose=2)grid_search.fit(my_data, my_labels, verbose=False)

我在测试数据上得到的示例输出如下：

Fitting 3 folds for each of 5 candidates, totalling 15 fits[CV] C=0.1 ...........................................................[CV] ............................................ C=0.1, total=   0.0s[CV] C=0.1 ...........................................................[CV] ............................................ C=0.1, total=   0.0s[CV] C=0.1 ...........................................................[CV] ............................................ C=0.1, total=   0.0s[CV] C=0.5 ...........................................................[CV] ............................................ C=0.5, total=   0.0s[CV] C=0.5 ...........................................................[CV] ............................................ C=0.5, total=   0.0s[CV] C=0.5 ...........................................................[CV] ............................................ C=0.5, total=   0.0s