Underfitting

Underfitting happens when a machine learning model is too simple to capture the underlying structure of data, leading to poor performance on both training and test sets.

This article explains the bias-variance tradeoff, helping founders balance simplicity and complexity in their predictive models to avoid common data pitfalls.