<ul data-eligibleForWebStory="true"><li>Small additive ensembles of symbolic rules that offer interpretable prediction models traditionally use rule conditions based on threshold propositions, resulting in axis-parallel polytopes as decision regions.</li><li>A new approach introduces logical propositions with learnable sparse linear transformations of input variables, enabling decision regions as general polytopes with oblique faces.</li><li>The proposed learning method utilizes a sequential greedy optimization based on logistic regression to efficiently construct rule ensembles with reduced model complexity across benchmark datasets.</li><li>Experimental results show that the new method achieves the same test risk as state-of-the-art methods while decreasing model complexity.</li></ul>

Interpretable Representation Learning for Additive Rule Ensembles

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