The Naive Bayes classifiers reside in the
sklearn.naive_bayes package. There are different kinds of Naive Bayes classifiers:
GaussianNB: This assumes the features to be normally distributed (Gaussian). One use case for it could be the classification of sex according to the given height and width of a person. In our case, we are given tweet texts from which we extract word counts. These are clearly not Gaussian distributed.
MultinomialNB: This assumes the features to be occurrence counts, which is relevant to us since we will be using word counts in the tweets as features. In practice, this classifier also works well with TF-IDF vectors.
BernoulliNB: This is similar to
MultinomialNB, but more suited when ...