Bias2

Bias2 is a term used in statistics and machine learning to denote the square of the bias component in model error. In the bias-variance decomposition of mean squared error (MSE), bias2 represents the squared difference between the expected model prediction and the true target value across possible training sets.

Notation and interpretation: Bias2 is typically written as bias^2 in mathematical notation. In some code or notes, it is labeled as bias2 for brevity. As a squared quantity, it is always non-negative and reflects systematic error arising from model assumptions, misspecification, or insufficient feature representation.

Causes and management: High bias2 can result from overly simple models, wrong functional form, or under-representation of the target signal. Reducing bias2 may involve increasing model complexity, adding informative features, or choosing a different learning algorithm; these changes can increase variance.

Relation to other concepts: Bias2 is one part of the fundamental equation MSE = bias2 + variance + irreducible error. Understanding bias2 helps diagnose underfitting and guides strategies to improve generalization by balancing bias and variance.

Notes: The term bias2 is not universally standardized. The conventional term is the squared bias, denoted bias^2, and "bias2" may appear in informal discussions, programming variable names, or educational materials as shorthand.