# Validate classification models

I have two Naïve Bayes classifiers

nb_classifier = MultinomialNB(alpha=0.05, fit_prior=True)
nb_classifier.fit(X_train, y_train)


and

nb_classifier = MultinomialNB(alpha=1, fit_prior=True)
nb_classifier.fit(X_train, y_train)


where the only difference is the alpha value.

How do I choose the classifier that performs best?

I guess I should both classifiers with my test data set, but what should I look for when I claim one of them to be better than the other?

Should I use

nb_classifier.score(X_test, y_test)


skl_cv.cross_val_predict(nb_classifier, X = X, y = y, cv = 5)