Machine Learning for OpenCV 4 - Second Edition

Classifying the data with a Naive Bayes classifier

The following steps will help you build a Naive Bayes classifier:

1. We can compare the result to a true Naive Bayes classifier by asking scikit-learn for help:

In [13]: from sklearn import naive_bayes...      model_naive = naive_bayes.GaussianNB()

2. As usual, training the classifier is done via the fit method:

In [14]: model_naive.fit(X_train, y_train)Out[14]: GaussianNB(priors=None)

3. Scoring the classifier is built in:

In [15]: model_naive.score(X_test, y_test)Out[15]: 1.0

4. Again a perfect score! However, in contrast to OpenCV, this classifier's predict_proba method returns true probability values, because all values are between 0 and 1 and because all rows add up to 1:

In [16]: yprob = model_naive.predict_proba(X_test) ...