Hi, Xgboost experts.
Does xgboost change in any manner when category features are label encoded? I worry that it would change forecasts and bring some ordinality into the data.
This is recommended by the documentation, which also recommends applying methods like hash encoding for categories with extremely large cardinality.
Howdy mates
The number of categories, the target model performance, and the particular dataset all influence the optimal strategy.
Try out various encoding techniques to determine which one best fits your situation.
To comprehend how categorical features affect the model, think about applying strategies like feature significance.
XGBoost does handle label encoding just fine, but it can introduce some unintended order into your categories, which might mess with your forecasts. Think of it like giving numbers to categories and assuming they have a natural order, even if they don’t. For big categories, hash encoding can be a lifesaver—it’s like turning a massive playlist into a manageable one without losing the vibe.