Overfitting vs Underfitting in ML: Causes, Diagnosis, and Fixes

This post details the causes, diagnosis, and fixes for overfitting and underfitting in machine learning models. It explains the bias-variance tradeoff and provides practical strategies for addressing these issues, including regularization, early stopping, data augmentation, cross-validation, and ensemble methods for overfitting, and increasing model complexity, adding critical features, and adjusting regularization for underfitting. It also discusses learning curves and how to iterate faster on model evaluation.