Peculiarities of the Hybrid Model Built Using Parallel Data

A hybrid model built using parallel data combines the prerequisites of two or more models in its predecessors, it becomes necessary to reduce the number of these predecessors. For this, we consider it necessary to investigate the cause-and-effect relationships between the preconditions, which are the “main” and cause other precursors, which should become the subject of research and the main decisive factor in the process of making predictions. A hybrid model can only be learned from “necessary” models, that is why it is important to study the predecessor, to identify the “necessary” and “sufficient” predecessor.