Classical ML Never Left: Why Interpretability Still Beats Raw Power

Classical machine learning remains invaluable for practitioners working with structured data because it prioritizes interpretability and transparent decision-making over raw performance. Unlike deep learning's opaque approach, classical ML demands explicit feature engineering and clear model behavior—critical requirements for applications where explainability, trust, and practical understanding matter more than marginal accuracy gains.