juniorNaïve Bayes
What are the assumptions of Naïve Bayes?
Updated May 17, 2026
Short answer
Naïve Bayes assumes feature independence and equal contribution of features within a class.
Deep explanation
The key assumption is conditional independence: features are independent given the class label. It also assumes feature contributions are multiplicative in likelihood computation. While unrealistic, this assumption simplifies computation and avoids complex covariance estimation.
Real-world example
Text classification where each word is treated independently.
Common mistakes
- Trying to use Naïve Bayes for strongly correlated numerical features.
Follow-up questions
- What happens if features are correlated?
- How can we reduce correlation impact?