Bias

there is exactly one theorem in machine learning that every practitioner rederives on a whiteboard at least once a year, and this is it. 𐃏 the squared-error risk of any learned predictor splits into three non-negative pieces — irreducible noise, squared bias, and variance — and every design decision you make (model class, regularisation strength, \(k\), ensemble size, early stopping) is secretly a transaction between the last two.