Machine Learning for OpenCV 4 - Second Edition

Training on the full dataset

However, if you want to classify the full dataset, we need a more sophisticated approach. We turn to scikit-learn's Naive Bayes classifier, as it understands how to handle sparse matrices. In fact, if you didn't pay attention and treated X_train like every NumPy array before, you might not even notice that anything is different:

In [17]: from sklearn import naive_bayes...      model_naive = naive_bayes.MultinomialNB()...      model_naive.fit(X_train, y_train)Out[17]: MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)

Here, we used MultinomialNB from the naive_bayes module, which is the version of Naive Bayes classifier that is best suited to handling categorical data, such as word counts.

The classifier is trained ...