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Will XGBoost pose any problem while dealing with categorical variables with more than 2 levels. For example, occupation variable can have values like doctor, engineer, lawyer, data scientist, farmer e.t.c. If so what would be a better method to use in that case?

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I think you should be more specific about what you mean by "fail". As an example, a practitioner could consider an xgboost model as a failure if it achieves < 80% accuracy.

Nevertheless, there are some annoying quirks in xgboost which similar packages don't suffer from:

  1. xgboost can't handle categorical features while lightgbm and catboost can.
  2. xgboost can be more memory-hungry than lightgbm (although this can be mitigated).
  3. xgboost can be slower than lightgbm.
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