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To study the influence of a single (hyper-)parameter, I use validation_curve:

....
grid_result = grid_obj.fit(X_train1, y_train1)
grid_best = grid_result.best_estimator_
train_score, val_score = validation_curve(grid_best 
                                          , X_train1 
                                          , y_train1
                                          , param_name = 'learning_rate'
                                          , param_range = param_range 
                                          , cv=5
                                          , scoring="neg_mean_squared_error" 
                                          , verbose = 1
                                         )

But param_name can hold only a single variable. Thus, though when e.g. already performing a hyperparameter search through gridsearchcv or something similar, I have to fit/CV all single parameters in validation_curve again. There is probably a smarter way, or?

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