What are the limitations while using XGboost algorithm?












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I was going through different kinds of boosting techniques such as Adaboost, gradient boosting and XGBoost. But i could not find the limitations of XGBoost apart from the fact that it overfits if the model is not stopped early which is the case for any tree based model.



Could someone suggest under what circumstances will XGboost fail






machine-learning xgboost data-science-model boosting







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    $begingroup$


    I was going through different kinds of boosting techniques such as Adaboost, gradient boosting and XGBoost. But i could not find the limitations of XGBoost apart from the fact that it overfits if the model is not stopped early which is the case for any tree based model.



    Could someone suggest under what circumstances will XGboost fail






    machine-learning xgboost data-science-model boosting







    share|improve this question







    New contributor




    Akhilesh Narapareddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      I was going through different kinds of boosting techniques such as Adaboost, gradient boosting and XGBoost. But i could not find the limitations of XGBoost apart from the fact that it overfits if the model is not stopped early which is the case for any tree based model.



      Could someone suggest under what circumstances will XGboost fail






      machine-learning xgboost data-science-model boosting







      share|improve this question







      New contributor




      Akhilesh Narapareddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I was going through different kinds of boosting techniques such as Adaboost, gradient boosting and XGBoost. But i could not find the limitations of XGBoost apart from the fact that it overfits if the model is not stopped early which is the case for any tree based model.



      Could someone suggest under what circumstances will XGboost fail






      machine-learning xgboost data-science-model boosting




      machine-learning xgboost data-science-model boosting






      share|improve this question







      New contributor




      Akhilesh Narapareddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Akhilesh Narapareddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      share|improve this question






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      asked yesterday









      Akhilesh NarapareddyAkhilesh Narapareddy

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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.






          share|improve this answer









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            $begingroup$

            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.






            share|improve this answer









            $endgroup$


















              0












              $begingroup$

              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.






              share|improve this answer









              $endgroup$
















                0












                0








                0





                $begingroup$

                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.






                share|improve this answer









                $endgroup$



                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.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 17 hours ago









                bradSbradS

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