Phase 1: Classification Algorithms · ~75 minutes · Python

Naive Bayes

The "naive" assumption is wrong, and it works anyway. That's the beauty of it.

What you will learn

Implement Multinomial Naive Bayes from scratch with Laplace smoothing for text classification
Explain why the naive independence assumption is mathematically wrong but produces correct class rankings in practice
Compare Multinomial, Bernoulli, and Gaussian Naive Bayes variants and select the right one for a given feature type
Evaluate Naive Bayes against logistic regression on high-dimensional sparse data and explain the bias-variance tradeoff at work

Introduction

Type: Build
Language: Python
Prerequisites: Bayes' Theorem
Time: ~75 minutes

Objective

Learning objectives

Implement Multinomial Naive Bayes from scratch with Laplace smoothing for text classification
Explain why the naive independence assumption is mathematically wrong but produces correct class rankings in practice
Compare Multinomial, Bernoulli, and Gaussian Naive Bayes variants and select the right one for a given feature type
Evaluate Naive Bayes against logistic regression on high-dimensional sparse data and explain the bias-variance tradeoff at work

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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