How to do it…

Let's see how to evaluate model accuracy using cross-validation metrics:

1. We will use the classifier just used in the Building a Naive Bayes classifier recipe (load the naive_bayes.py file). We will start with the accuracy measure:

from sklearn import model_selection
num_validations = 5
accuracy = model_selection.cross_val_score(classifier_gaussiannb,
        X, y, scoring='accuracy', cv=num_validations)
print "Accuracy: " + str(round(100*accuracy.mean(), 2)) + "%"

1. We will use the preceding function to compute precision, recall, and the F1 score as well:

f1 = model_selection.cross_val_score(classifier_gaussiannb,
 X, y, scoring='f1_weighted', cv=num_validations)
print "F1: " + str(round(100*f1.mean(), 2)) + "%"
precision = model_selection.cross_val_score(classifier_gaussiannb,
 X, y, scoring='precision_weighted', cv=num_validations)
print "Precision: " + str(round(100*precision.mean(), 2)) + "%"
recall = model_selection.cross_val_score(classifier_gaussiannb,
 X, y, scoring='recall_weighted', cv=num_validations)
print "Recall: " + str(round(100*recall.mean(), 2)) + "%"