1. A computer-implemented method for interpolating isotonic regression in a binary classifier system, comprising: receiving a set of training examples for content items, each training example including a positive or negative label for a classification and a value for each of D attributes of the content items, wherein the values are provided as D-dimensional coordinates; determining an interpolation function based on a Delaunay triangulation of coordinates in the received training set, wherein the determined function satisfies monotonicity constraints and includes maximizing a likelihood of an observation subject to the monotonicity constraints; receiving the observation for a content item, the observation including D-dimensional coordinates; and determining a probability that the observation is positive for the classification based on the determined interpolation function.