The predominance of machine learning models in many spheres of human activity has led to a growing demand for their transparency. The transparency of models makes it possible to discern some factors, such as security or non-discrimination. In this paper, we propose a mixture of transparent local models as an alternative solution for designing interpretable (or transparent) models. Our approach is designed for the situations where a simple and transparent function is suitable for modeling the label of instances in some localities/regions of the input space, but may change abruptly as we move from one locality to another. Consequently, the proposed algorithm is to learn both the transparent labeling function and the locality of the input space where the labeling function achieves a small risk in its assigned locality. By using a new multi-predictor (and multi-locality) loss function, we established rigorous PAC-Bayesian risk bounds for the case of binary linear classification problem and that of linear regression. In both cases, synthetic data sets were used to illustrate how the learning algorithms work. The results obtained from real data sets highlight the competitiveness of our approach compared to other existing methods as well as certain opaque models. Keywords: PAC-Bayes, risk bounds, local models, transparent models, mixtures of local transparent models.

翻译：机器学习模型在人类诸多活动领域的主导地位引发了对模型透明性日益增长的需求。模型透明性有助于识别安全性和非歧视性等重要因素。本文提出一种局部透明模型混合方法，作为设计可解释（或透明）模型的替代方案。该方法适用于以下场景：在输入空间的某些局部区域，简单透明的函数足以建模实例标签，但当从一个局部区域转移到另一区域时，该函数可能发生突变。因此，所提算法需要同时学习透明标注函数及其在输入空间中的有效区域，确保标注函数在指定区域内实现较低风险。通过采用新型多预测器（多区域）损失函数，我们为二元线性分类问题和线性回归问题建立了严格的PAC-贝叶斯风险界。两种情况下均使用合成数据集演示学习算法的工作原理。真实数据集的实验结果表明，相较于现有方法及某些不透明模型，本方法具有显著竞争力。关键词：PAC-贝叶斯、风险界、局部模型、透明模型、局部透明模型混合。