Naive Bayes
The "naive" assumption is wrong, and it works anyway. That's the beauty of it.
Prerequisites: Phase 2, Lessons 01-07 (classification, Bayes' theorem)
#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
Keep going. The rest of this lesson is one step away.
Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.
The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.