Interpretability is a crucial aspect of machine learning models that enables
humans to understand and trust the decision-making process of these models. In
many real-world applications, the interpretability of models is essential for
legal, ethical, and practical reasons. For instance, in the banking domain,
interpretability is critical for lenders and borrowers to understand the
reasoning behind the acceptance or rejection of loan applications as per fair
lending laws. However, achieving interpretability in machine learning models is
challenging, especially for complex high-performance models. Hence Explainable
Boosting Machines (EBMs) have been gaining popularity due to their
interpretable and high-performance nature in various prediction tasks. However,
these models can suffer from issues such as spurious interactions with
redundant features and single-feature dominance across all interactions, which
can affect the interpretability and reliability of the model's predictions. In
this paper, we explore novel approaches to address these issues by utilizing
alternate Cross-feature selection, ensemble features and model configuration
alteration techniques. Our approach involves a multi-step feature selection
procedure that selects a set of candidate features, ensemble features and then
benchmark the same using the EBM model. We evaluate our method on three
benchmark datasets and show that the alternate techniques outperform vanilla
EBM methods, while providing better interpretability and feature selection
stability, and improving the model's predictive performance. Moreover, we show
that our approach can identify meaningful interactions and reduce the dominance
of single features in the model's predictions, leading to more reliable and
interpretable models.
Index Terms- Interpretability, EBM's, ensemble, feature selection.

通过使用替代交叉功能选择、集成功能和模型配置技术等新方法，改进了可解释性较强的 EBM 模型，以提高其可靠性和解释性，并在三个基准数据集上进行了验证。

交叉特征选择消除虚假交互作用和单特征占主导地位的可解释提升机

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

Energy-Based Models (EBMs) present a flexible and appealing way to represent
uncertainty. Despite recent advances, training EBMs on high-dimensional data
remains a challenging problem as the state-of-the-art approaches are costly,
unstable, and require considerable tuning and domain expertise to apply
successfully. In this work, we present a simple method for training EBMs at
scale which uses an entropy-regularized generator to amortize the MCMC sampling
typically used in EBM training. We improve upon prior MCMC-based entropy
regularization methods with a fast variational approximation. We demonstrate
the effectiveness of our approach by using it to train tractable likelihood
models. Next, we apply our estimator to the recently proposed Joint Energy
Model (JEM), where we match the original performance with faster and stable
training. This allows us to extend JEM models to semi-supervised classification
on tabular data from a variety of continuous domains.

本文提出了一种简单的方法来训练基于能量的模型（EBMs），并将其应用于半监督分类，方法利用熵正则化的生成器来分摊在 EBM 训练中通常使用的 MCMC 采样，这使得实验结果更快、稳定和可行。