Machine Learning Tutorial

MultinomialNB

MultinomialNB is applied to count data. It has a single parameter, alpha, which controls model complexity. The way alpha works is that the algorithm adds to the data alpha many virtual data points that have positive values for all the features. This results in a “smoothing” of the statistics. A large alpha means more smoothing, resulting in less complex models. The algorithm’s performance is relatively robust to the setting of alpha, meaning that setting alpha is not critical for good performance. However, tuning it usually improves accuracy somewhat.

from sklearn.naive_bayes import MultinomialNB

model = MultinomialNB().fit(X, y)