Discretization refers to the process of binning a continuous variable into a discrete number of buckets. In some machine learning algorithms, the performance can be improved by using this kind of representation, especially if there are outliers on the original scale of the variable that cause its distribution to be skewed. There are different ways to perform discretization, but common approaches include equal width bins, where the spacing between endpoints is constant; equal size bins, where each bin contains roughly equal number of observations even if the endpoints are not spaced uniformly; or using a decision tree to create bins that are most predictive of the target variable.