  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.
