What Is Underfitting in Machine Learning?

This post details what underfitting looks like in practice, explaining it as a model that hasn't learned relevant patterns and performs poorly on both training and test sets, characterized by high bias. It contrasts underfitting with overfitting by explaining the bias-variance tradeoff and how learning curves illustrate this. The post highlights why underfitting is harder to catch than overfitting, often leading to anti-patterns like "just needing more data" or cycling through algorithms without auditing features. It outlines a structured iteration loop for fixing underfitting: increasing model capacity, reducing regularization, engineering better features, improving data quality, and tuning hyperparameters. It also discusses the dangers of AI analytics meeting underfit models, leading to plausible but meaningless outputs, and emphasizes the importance of integrating model diagnostics into the explanation pipeline. Finally, it notes that fixing underfitting often exposes overfitting, and managing this tension is the core job of ML practitioners.