Searching for best parameters

grid-search.py

from sklearn.grid_search import GridSearchCV

dtc = DecisionTreeClassifier()

parameter_grid = {'criterion': ['gini', 'entropy'],
                  'splitter': ['best', 'random'],
                  'max_depth': [1, 2, 3, 4, 5],
                  'max_features': [1, 2, 3, 4]}

cross_validation = StratifiedKFold(all_classes, n_folds=10)

grid_search = GridSearchCV(dct, param_grid=parameter_grid, cv=cross_validation)

grid_search.fit(all_inputs, all_classes)
print('Best score: {}'.format(grid_search.best_score_))
print('Best parameters: {}'.format(grid_search.best_params_))

dtc = grid_search.best_estimator_
dtc