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From my understanding:
best_estimator_ provides the estimator with highest score; best_score_ provides the score of the selected estimator; cv_results_ may be exploited to get the scores of all estimators.
However, it is not clear to me how to get the estimators themselves.
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The other two parameters in the grid search is where the limitations come in to play. The results of GridSearchCV can be somewhat misleading the first time around. The best combination of parameters found is more of a conditional “best” combination. This is due to the fact that the search can only test the parameters that you fed into param_grid.
Obviously, nothing is perfect and GridSearchCV is no exception: The “best” parameters that GridSearchCV identifies are technically the best that could be produced, but only by the parameters that you included in your parameter grid.
In a grid search, you try a grid of hyper-parameters and evaluate the performance of each combination of hyper-parameters. How does Sklearn’s GridSearchCV Work? The GridSearchCV class in Sklearn serves a dual purpose in tuning your model. The class allows you to: This tutorial won’t go into the details of k-fold cross validation.
To get the best set of hyperparameters we can use Grid Search. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Finally it gives us the set of hyperparemeters which gives the best result after passing in the model. 1. Imports the necessary libraries
As I see it, you cannot. But what you can do is taking the best parameter combination from best_params_ and fit the model again with those same parameters.
Check out attributes of GridSearchCV
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