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Interpretability is a crucial aspect of machine learning models that enableshumans to understand and trust the decision-making process of these models. Inmany real-world applications, the interpretability of models is essential forlegal, ethical, and practical reasons. For instance, in the banking domain,interpretability is critical for lenders and borrowers to understand thereasoning behind the acceptance or rejection of loan applications as per fairlending laws. However, achieving interpretability in machine learning models ischallenging, especially for complex high-performance models. Hence ExplainableBoosting Machines (EBMs) have been gaining popularity due to theirinterpretable and high-performance nature in various prediction tasks. However,these models can suffer from issues such as spurious interactions withredundant features and single-feature dominance across all interactions, whichcan affect the interpretability and reliability of the model's predictions. Inthis paper, we explore novel approaches to address these issues by utilizingalternate Cross-feature selection, ensemble features and model configurationalteration techniques. Our approach involves a multi-step feature selectionprocedure that selects a set of candidate features, ensemble features and thenbenchmark the same using the EBM model. We evaluate our method on threebenchmark datasets and show that the alternate techniques outperform vanillaEBM methods, while providing better interpretability and feature selectionstability, and improving the model's predictive performance. Moreover, we showthat our approach can identify meaningful interactions and reduce the dominanceof single features in the model's predictions, leading to more reliable andinterpretable models.  Index Terms- Interpretability, EBM's, ensemble, feature selection.

Cross Feature Selection to Eliminate Spurious Interactions and Single Feature Dominance Explainable Boosting Machines