FIG. 6. Bias-Variance tradeoff.

Another useful depiction of the bias-variance tradeoff is to think about how E_out varies as we consider different training data sets of a fixed size. A more complex model (green) will exhibit larger fluctuations (variance) due to finite size sampling effects than the simpler model (black). However, the average over all the trained models (bias) is closer to the true model for the more complex model.