what does regularization mean in xgboost (tree)












3












$begingroup$


In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.



Can someone explain how regularization works in xgbtree?






r regularization xgboost







share|cite|improve this question











$endgroup$

















    3












    $begingroup$


    In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.



    Can someone explain how regularization works in xgbtree?






    r regularization xgboost







    share|cite|improve this question











    $endgroup$















      3












      3








      3





      $begingroup$


      In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.



      Can someone explain how regularization works in xgbtree?






      r regularization xgboost







      share|cite|improve this question











      $endgroup$




      In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context of tree-based methods, I'm not sure how regularization works.



      Can someone explain how regularization works in xgbtree?






      r regularization xgboost




      r regularization xgboost






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited 7 hours ago









      kiamlaluno

      1034




      1034










      asked 11 hours ago









      zeslazesla

      20218




      20218






















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          $begingroup$

          In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:




          Minimum loss reduction required to make a further partition on a leaf
          node of the tree. The larger gamma is, the more conservative the
          algorithm will be.




          Source: https://xgboost.readthedocs.io/en/latest/parameter.html



          This minimum gain can usually be set for anything between $(0,infty)$.



          Here's a somewhat good article on how to tune regularization on XGBoost.






          share|cite|improve this answer











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            $begingroup$

            In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:




            Minimum loss reduction required to make a further partition on a leaf
            node of the tree. The larger gamma is, the more conservative the
            algorithm will be.




            Source: https://xgboost.readthedocs.io/en/latest/parameter.html



            This minimum gain can usually be set for anything between $(0,infty)$.



            Here's a somewhat good article on how to tune regularization on XGBoost.






            share|cite|improve this answer











            $endgroup$


















              2












              $begingroup$

              In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:




              Minimum loss reduction required to make a further partition on a leaf
              node of the tree. The larger gamma is, the more conservative the
              algorithm will be.




              Source: https://xgboost.readthedocs.io/en/latest/parameter.html



              This minimum gain can usually be set for anything between $(0,infty)$.



              Here's a somewhat good article on how to tune regularization on XGBoost.






              share|cite|improve this answer











              $endgroup$
















                2












                2








                2





                $begingroup$

                In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:




                Minimum loss reduction required to make a further partition on a leaf
                node of the tree. The larger gamma is, the more conservative the
                algorithm will be.




                Source: https://xgboost.readthedocs.io/en/latest/parameter.html



                This minimum gain can usually be set for anything between $(0,infty)$.



                Here's a somewhat good article on how to tune regularization on XGBoost.






                share|cite|improve this answer











                $endgroup$



                In tree-based methods regularization is usually understood as defining a minimum gain so which another split happens:




                Minimum loss reduction required to make a further partition on a leaf
                node of the tree. The larger gamma is, the more conservative the
                algorithm will be.




                Source: https://xgboost.readthedocs.io/en/latest/parameter.html



                This minimum gain can usually be set for anything between $(0,infty)$.



                Here's a somewhat good article on how to tune regularization on XGBoost.







                share|cite|improve this answer














                share|cite|improve this answer



                share|cite|improve this answer








                edited 10 hours ago

























                answered 10 hours ago









                Lucas FariasLucas Farias

                642421




                642421






























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