 Minimum redundancy feature selection

Minimum Redundancy Feature Selection (MRMR) is a feature selection method used primarily in machine learning and data mining to select a subset of relevant features from a larger set while minimizing redundancy among those features. The goal is to identify the most informative features that contribute to the predictive power of the model without introducing unnecessary overlap among the selected features. ### Key Concepts: 1. **Relevance**: Features that have a strong relationship with the target variable are considered relevant.