Issue with Categorical Feature Encoding in Binary Classification about catboost HOT 2 OPEN

Hello!
It seems that in your case (there are very few different values in your cat feature), the best options is to use one_hot_encoded features (set option one_hot_max_size to 100). We will check, why it is not default behaviour in your case.

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Hi, Ekaterina,

Thank you for your response.

One-hot encoding definitely works in this toy case.
However, I am still concerned about real production cases. Is it possible that the issues we've discussed could happen with real data?
I am worried because, despite all my research, I haven't found a comprehensive guide on categorical feature encoding. If I understand correctly, the CatBoost categorical encoding algorithm uses a variety of encoding schemes that operate differently depending on many parameters, such as whether it's a classification or regression task, among others.
Do you have any kind of diagram that describes the algorithm for choosing parameters for categorical encoding?
I've heard many people say: "We don't know exactly how it works, but it works." Perhaps if we describe this algorithm in more detail, we could add it to the documentation and help more people feel confident about using CatBoost.

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