ИСТИНА

The paper deals with the issues of formal verification of machine learning systems. The demand for confirmation of the stability of ML systems is growing. How will the built ML system perform on data that is different from the set on which it was trained? Is it possible to somehow verify or even prove that the behavior of the system, which was demonstrated on the initial dataset, will always remain so? There are different ways to try to do this.