What is bias in machine learning?

Bias is the error introduced by approximating a real-world problem with a simplified model, leading to systematic prediction errors.

Bias represents how far the model’s predictions are from the true underlying function. High bias typically occurs when a model is too simple to capture patterns in data, such as linear models applied to nonlinear problems. It reflects assumptions built into the learning algorithm that restrict flexibility. In bias-variance tradeoff, increasing model complexity reduces bias but may increase variance.