What are the limitations while using XGboost algorithm?
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 &lt; 80% accuracy.

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


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