Variance marks the sensitivity of a model towards the training dataset. As we know, the learning phase relies on a small subset of all possible data combinations called the training set. Thus, variance error captures the changes in the model's estimates as the training dataset changes.

Low variance suggests significantly fewer changes to prediction values, as the underlying training dataset changes while high variance points in the other direction. Non-parametric algorithms such as decision trees have high variance, while parametric algorithms such as linear regression are less flexible and hence low on variance.