Checkered Regression

Abstract This paper introduces the checkered regression model, a nonlinear generalization of logistic regression. While the decision boundary of logistic regression consists of a single hyperplane, our method is shown to tessellate the feature space by any given number of hyperplanes. In order to fit the model’s parameters to some labeled data, we describe a classic empirical risk minimization framework based on the cross entropy loss. A multiclass version of our approach is also proposed.