 One last detail that we have skipped over is the prediction error of \(h(X)\) due to noise, N, which occurs independent of the model/learning algorithm used. Thus, the full bias-variance decomposition of the mean-squared error of an estimate h is the sum of the bias (squared), variance, and irreducible error: 