In modern data science, it is often not enough to obtain only a data-driven model with a good prediction quality. On the contrary, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. Such questions are unified under machine learning interpretability questions, which could be considered one of the area's raising topics. In the paper, we use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties. It means that whereas one of the apparent objectives is precision, the other could be chosen as the complexity of the model, robustness, and many others. The method application is shown on examples of multi-objective learning of composite models, differential equations, and closed-form algebraic expressions are unified and form approach for model-agnostic learning of the interpretable models.
翻译:在现代数据科学中,仅仅获得具有良好预测质量的数据驱动模型往往是不够的,相反,更有趣的是了解模型的特性,哪些部分可以替换,以获得更好的结果。这些问题在机器学习可解释性问题下是统一的,这可以被视为该领域提出的议题之一。在论文中,我们使用多目标进化优化来综合数据驱动模型学习,以获得算法的预期特性。这意味着,一个明显的目标是精确的,而另一个则可以选择为模型的复杂性、稳健性和其他许多方面。方法应用在复合模型、差异方程式和封闭形代数表达的多目标学习实例中展示,对可解释模型进行模型-不可知性学习的方式是统一的。