What is feature selection using mutual information?
Updated May 16, 2026
Short answer
Mutual information feature selection measures how much information a feature shares with the target variable.
Deep explanation
Mutual information quantifies dependency between variables, capturing both linear and nonlinear relationships. Unlike correlation, it does not assume linearity. Features with higher mutual information scores are more informative for prediction tasks.
Real-world example
Used in fraud detection to identify transaction features strongly linked to fraud outcomes.
Common mistakes
- Confusing mutual information with correlation and ignoring feature scaling effects.
Follow-up questions
- How is mutual information different from correlation?
- Can MI detect nonlinear relationships?