Title: Bernoulli Naive Bayes Classifier
Slug: bernoulli_naive_bayes_classifier
Summary: How to train a Bernoulli naive bayes classifer in Scikit-Learn
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Naive Bayes
Authors: Chris Albon

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



In [1]:

    
# Load libraries
import numpy as np
from sklearn.naive_bayes import BernoulliNB

Create Binary Feature And Target Data



In [2]:

    
# 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



In [7]:

    
# View first ten observations
X[0:10]









    Out[7]:





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



In [8]:

    
# 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)