The Art of Feature Selection in AI Models (5 Minutes)

Feature selection is a crucial step in AI that identifies important variables in datasets to enhance model performance, reduce computational costs, and improve interpretability. Techniques include filter methods (e.g., Pearson's Correlation), wrapper methods (e.g., Forward Selection), and embedded methods (e.g., LASSO). Effective feature selection minimizes overfitting and helps create simpler, more robust models while aligning with domain knowledge for better insights.

#FeatureSelection #DataScience #MachineLearning #AI #ModelPerformance