How to get average precision, recall, f1, accuracy of cross-validation with sklearn?

Question

I am using k-fold cross validation for hyperparameter tuning on the whole training set with Weka and it shows the average precision, recall, f1 of cross validation. I want to get the same results with Sklearn in python.

    cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)
    grid = GridSearchCV(LinearSVC(), param_grid=param_grid, cv=cv)
    grid.fit(X_train, y_train)
    # print the best parameters
    print("The best parameters are %s with a score of %0.5f"
      % (grid.best_params_, grid.best_score_))
    # print the average precision, recall, f1, accuracy of cross 
    # validation with the best parameters found
    ???

Anyone can help?

Answer 1

You can use the classification_report function from sklearn.metrics .

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html