Why do linear models typically have high bias?

Updated May 15, 2026

Short answer

Linear models have high bias because they assume a linear relationship between features and target.

Deep explanation

Linear models are constrained to represent only linear relationships. If the true data relationship is nonlinear, these models cannot capture it effectively, resulting in systematic errors. This restriction introduces bias because the model is too simple to represent complex patterns.

Real-world example

Using linear regression for predicting user engagement that depends on nonlinear behavioral patterns.

Common mistakes

Assuming linear models are always weak models.

Follow-up questions

Can linear models be improved?
Do linear models ever overfit?

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