Cross-validation in sklearn: do I need to call fit() as well as cross_val_score()?

Question

I would like to use k-fold cross validation while learning a model. So far I am doing it like this:

# splitting dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(dataset_1, df1['label'], test_size=0.25, random_state=4222)

# learning a model
model = MultinomialNB()
model.fit(X_train, y_train)
scores = cross_val_score(model, X_train, y_train, cv=5)

At this step I am not quite sure whether I should use model.fit() or not, because in the official documentation of sklearn they do not fit but just call cross_val_score as following (they do not even split the data into training and test sets):

from sklearn.model_selection import cross_val_score
clf = svm.SVC(kernel='linear', C=1)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)

I would like to tune the hyper parameters of the model while learning the model. What is the right pipeline?

Answer 1

Your second example is right for doing the cross validation. See the example here: http://scikit-learn.org/stable/modules/cross_validation.html#computing-cross-validated-metrics

The fitting will be done inside the cross_val_score function, you don't need to worry about this beforehand.

[Edited] If, besides cross validation, you want to train a model, you can call model.fit() afterwards.