f1 score for test data

Question

To get the best f1 score of cross validation I do that

grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=10, verbose=10, scoring='f1')
grid_result = grid_search.fit(X_train, y_train)

print("best parameters", grid_search.best_params_)
print('Best score : {}'.format(grid_search.best_score_))

but for the Test score I also need f1-score not the accuracy

print("Test Score",grid_search.best_estimator_.score(X_test,y_test.reshape(y_test.shape[0])))

Is there any function eg, f1_score() that I can use or should I write the function myself?

Answer 1

You can compute the f1 score with:

• the classification report ( example here )

• the Scikit-learn f1_score function: ( http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html )