The bias-variance decomposition is derived.

简介：
The process of deriving the bias-variance decomposition involves a detailed examination of how systematic errors (bias) and random fluctuations (variance) contribute to a model’s overall performance. Specifically, this decomposition offers a method for understanding the trade-off between these two factors, which are crucial in achieving optimal predictive accuracy. By meticulously tracing the steps involved in this derivation, one can gain a deeper insight into the underlying principles governing model behavior and identify potential areas for improvement.