sklearn - Cross validation with multiple scores

Question

I would like to compute the recall, precision and f-measure of a cross validation test for different classifiers. scik

Answer 1

This might be helpful if you looking multiple-metrics with multi-classes. With the latest doc in scikit learn 0.19 and above; you can pass your own dictionary with metric functions;

custom_scorer = {'accuracy': make_scorer(accuracy_score),
                 'balanced_accuracy': make_scorer(balanced_accuracy_score),
                 'precision': make_scorer(precision_score, average='macro'),
                 'recall': make_scorer(recall_score, average='macro'),
                 'f1': make_scorer(f1_score, average='macro'),
                 }
scores = cross_validation.cross_val_score(clf, X_train, y_train,
        cv = 10, scoring = custom_scorer)