# Relationship between bias, variance, and regularization

In Goodfellow et al.'s Deep Learning, the authors write on page 222:

"... the model family being trained either (1) excluded the true data-generating process - corresponding to underfitting and inducing bias, or (2) matched the true data-generating process, or (3) included the generating process but also many other possible generating processes - the overfitting regime where variance rather than bias dominates the estimation error. The goal of regularization is to take a model from the third into the second regime."

I'm wondering why the case of underfitting induces bias and why overfitting increases variance. The connection between the two is not clear.

Also, I come from a Bayesian inference background, and I think of regularization as a sort of prior that causes the parameters to shift towards a prior belief. With that interpretation, why can regularization not be used to correct both overfitting and underfitting?

Edit: I'm also confused about where the bias and variance come in. Are they in reference to the parameter values or the predictions? And if the former, how can you even compare in terms of bias or variance a fitted model to the true model (if you knew it) if the fitted model has a different number of parameters than the true model and/or if these parameters are weighting different features computed from the raw predictors? If the latter, how can you be certain that an underfitted model will have high bias (and vice versa)? For example, in figure 2.11 in the ISLR book referenced in @Alex's answer, it looks like, for the linear model, half then predictions lie above the line and half below, so wouldn't the bias be zero?

in short, plz check out pictures • well, below or/and above doesnt matter much if you look on MSE: ${MSE=\frac{1}{n}\sum_{i=1}^{n}(\hat{Y}_i - Y_i)^2}$ where ${Y_i}$ with hat is the estimation and without is the true value. Regarding the second question checkout wikipedia page "Bias–variance tradeoff", in short there is an irreducible error (noise), bias (discrepancy between model and real process) and variance (sensitivity to small fluctuation). So when an algo is too sensitive to fluctuation it collects noise in the model (overfitting), when it's not enough flexible it brings bigger bias (underfitting). – Alex Nikiforov Aug 30 '17 at 18:13