Machine Learning Algorithms

Bernoulli naive Bayes

If X is random variable and is Bernoulli-distributed, it can assume only two values (for simplicity, let's call them 0 and 1) and their probability is:

To try this algorithm with scikit-learn, we're going to generate a dummy dataset. Bernoulli naive Bayes expects binary feature vectors; however, the class BernoulliNB has a binarize parameter, which allows us to specify a threshold that will be used internally to transform the features:

from sklearn.datasets import make_classification>>> nb_samples = 300>>> X, Y = make_classification(n_samples=nb_samples, n_features=2, n_informative=2, n_redundant=0)

We have generated ...