We will use sklearn’s built-in accuracy and confusion matrix to look at how well our naïve Bayes models are performing: # compare predictions to true labels from sklearn import metricsprint metrics.accuracy_score(y_test, preds) print metrics.confusion_matrix(y_test, preds) The output is as follows: accuracy == 0.988513998564confusion matrix ==[[12035][11174]] First off, our accuracy is great! Compared to our null […]