How to get Best Estimator on GridSearchCV (Random Forest Classifier Scikit)

Question

I\'m running GridSearch CV to optimize the parameters of a classifier in scikit. Once I\'m done, I\'d like to know which parameters were chosen as the best.

Whenever

Answer 1

You have to fit your data before you can get the best parameter combination.

from sklearn.grid_search import GridSearchCV
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
                           n_features=10,
                           n_informative=3,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)


rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50, oob_score = True) 

param_grid = { 
    'n_estimators': [200, 700],
    'max_features': ['auto', 'sqrt', 'log2']
}

CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)
CV_rfc.fit(X, y)
print CV_rfc.best_params_

Answer 2

Just to add one more point to keep it clear.

The document says the following:

best_estimator_ : estimator or dict:

Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data.

When the grid search is called with various params, it chooses the one with the highest score based on the given scorer func. Best estimator gives the info of the params that resulted in the highest score.

Therefore, this can only be called after fitting the data.