What is overfitting? Causes, detection & prevention

This post details the causes, detection, and prevention of overfitting in machine learning models. It explains the bias-variance tradeoff, the problems caused by overfitting (clinical risks, operational noise, eroding stakeholder confidence), and common causes like insufficient data, noisy data, feature engineering mistakes, and inadequate regularization. Detection methods like learning curves, cross-validation, validation curves, early stopping, and production monitoring are discussed. Prevention strategies include regularization (L1, L2, dropout), data augmentation, early stopping, and architectural choices. The post highlights how Hex's platform supports these practices by enabling experimentation with regularization, visualization of learning curves, and tracking performance across iterations.