# Bernoulli Naive Bayes Classifier

The Bernoulli naive Bayes classifier assumes that all our features are binary such that they take only two values (e.g. a nominal categorical feature that has been one-hot encoded).

## Preliminaries

``````
import numpy as np
from sklearn.naive_bayes import BernoulliNB``````

## Create Binary Feature And Target Data

``````
/* Create three binary features */
X = np.random.randint(2, size=(100, 3))

/* Create a binary target vector */
y = np.random.randint(2, size=(100, 1)).ravel()``````

## View Feature Data

``````
/* View first ten observations */
X[0:10]``````
``````array([[1, 1, 1],
[0, 1, 0],
[1, 1, 1],
[0, 0, 0],
[1, 0, 1],
[1, 1, 1],
[0, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 0]])
``````

## Train Bernoulli Naive Bayes Classifier

``````
/* Create Bernoulli Naive Bayes object with prior probabilities of each class */
clf = BernoulliNB(class_prior=[0.25, 0.5])

/* Train model */
model = clf.fit(X, y)``````

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