Summary
CatBoost (short for “Categorical Boosting”) is a machine learning algorithm developed by Yandex, primarily designed for handling categorical data. It is an implementation of gradient boosting and is often used for classification and regression tasks.
Related Sections/Pre-Reading
Content
I won’t go into detail as to the maths of the categorical boosting model but rather the key principles of it.
Cat boosting uses a fancier version of target encoding to encode all discrete features within a dataset, then performs gradient boosting techniques to minimise the loss on sequentially generated weak learners to end up with an overall model
I highly recommend watching Josh Starmer’s video on it for a detailed explanation as he does it better than I could ever. Link