Introduction
When working with a large amount of data, it becomes necessary to compress the space with features into vectors. An example is text embeddings, which are an integral part of almost any NLP model creation process. Unfortunately, it is far from always possible to use neural networks to work with this type of data — the reason, for example, maybe a low fitting or inference rates.
I want to suggest an interesting way to use gradient boosting that few people know about.