Using a Pipeline with GridSearch

basic_pipe_gridsearch.py

from sklearn.model_selection import GridSearchCV

param_grid = {"estimator__n_estimators" : [100, 150, 200],
              "estimator__criterion" : ["gini", "entropy"],
              "estimator__max_depth" : [3, 4, 5]}

# You can change the scoring parameter here depending on which score you want to maximize
# You can also change the cv parameter to perform a cross validation with n folds for each model you fit
grid_rf = GridSearchCV(estimator= basic_pipe,
                       param_grid = param_grid,
                       scoring= "accuracy",
                       cv=5)

# Fit the gridsearch object to your training data
grid_rf.fit(X_train, y_train)

# See the mean cross-validated score of the best model
grid_rf.best_score_

# See the params of the best model
grid_rf.best_params_

# Store the best model in a variable to reference later
best_rf = grid_rf.best_estimator_