In this approach the error is decom

In this approach the error is decomposed into three components: model error
C (the difference between the optimal function approximation g, given the current
model, and the true function f ), bias B (the difference between the current approximation f and an optimal one g), and variance V (how much the function approximation f varies ). In other words, we have: