Feature Store

Feature Selection in machine learning involves identifying and choosing the most relevant variables that contribute significantly to the predictive power of a model. By eliminating irrelevant or redundant features, it improves model performance, reduces overfitting, and decreases computational cost. Techniques include filter methods, wrapper methods, and embedded methods, each evaluating features based on statistical metrics, model performance, or algorithm-based importance.