Modeling interactions in regression models poses both com- putational as well as statistical challenges: the computational resources and the amount of data required to solve them increases sharply with the size of the problem. We focus on logistic regression with categori- cal variables and propose a method for learning dependencies that are expressed as general Boolean formulas. The computational and statis- tical challenges are solved by applying a technique called transformed Lasso, which involves a matrix transformation of the original covariates. We compare the method to an earlier related method, LogicReg, and show that our method scales better in terms of the number of covariates as well as the order and complexity of the interactions.
['Yuan Zou', 'Teemu Roos']
Sparse Logistic Regression with Logical Features