 The bias-variance trade-off refers to a particular decomposition of overall prediction error for an estimator into its central tendency (bias) and dispersion (variance). Sometimes overall error can be reduced by increasing bias in order to reduce variance, or vice versa, effectively trading an increase in one type of error to afford a comparatively larger reduction in the other. To give an intuitive example, suppose your goal is to minimize your score with respect to the following targets.