How does underfitting relate to bias?

Updated May 15, 2026

Short answer

Underfitting occurs when a model has high bias and cannot capture underlying patterns in data.

Deep explanation

Underfitting happens when the model is too simple relative to the complexity of the data. It results in high training and test errors because the model cannot learn meaningful patterns. This is directly associated with high bias in the bias-variance framework.

Real-world example

Using linear regression for predicting nonlinear stock market trends.

Common mistakes

Assuming underfitting means the model is too small only.

Follow-up questions

How do you detect underfitting?
How do you fix underfitting?

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