Contrast trees are used to assess the accuracy of many types of machine learning estimates that are not amenable to standard validation techniques. These include properties of the conditional distribution $p_{y}(y,|,\mathbf{x})$ (means, quantiles, complete distribution) as functions of $\mathbf{x}$. Given a set of predictor variables $\mathbf{x}=(x_{1},x_{2},$$,x_{p})$ and two outcome variables $y$ and $z$ associated with each $\mathbf{x}$, a contrast tree attempts to partition the space of $\mathbf{x}$ values into local regions within which the respective distributions of $y,|,\mathbf{x}$ and $z,|,\mathbf{x}$, or selected properties of those distributions such as means or quantiles, are most different.
For more details, please see the tutorial.