Generalized Linear Rule Models
This work addresses the need for interpretable machine learning models that balance accuracy and complexity, though it is incremental in improving rule ensemble algorithms.
The paper tackles the problem of creating interpretable generalized linear models using rule ensembles for regression and classification, achieving better accuracy-complexity trade-offs than existing methods and competitive performance with less interpretable benchmarks.
This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set complexity and prediction accuracy. Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one. The column generation subproblem is solved using either integer programming or a heuristic optimizing the same objective. In experiments involving logistic and linear regression, the proposed methods obtain better accuracy-complexity trade-offs than existing rule ensemble algorithms. At one end of the trade-off, the methods are competitive with less interpretable benchmark models.