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An Introductory Guide to Data Science and Machine Learning

Naive Bayes: Part-2

Understand the different Naive Bayes classifiers such as Gaussian, Multinomial, Complement, Bernoulli, and Categorical. Learn how they apply to tasks like text classification and imbalanced datasets. Discover advantages, common challenges, and practical Python implementation to apply these models effectively.

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Types of Naive Bayes algorithms

%0 node_1 Naive Bayes node_2 Gaussian Naive Bayes node_1->node_2 node_3 Multinomial Naive Bayes node_1->node_3 node_4 Complement Naive Bayes node_1->node_4 node_1598196496337 Bernoulli Naive Bayes node_1->node_1598196496337
Naive Bayes methods

The different Naive Bayes classifiers differ mainly by the assumptions they make regarding the distribution of P(xi∣y)P(x_i | y) in the equation below:

y=argmaxyP(y)∏i=1nP(xi∣y)y = argmax_yP(y)\prod_{i=1}^{n}P(x_i|y) ...