Use prediction as feature for a decision tree












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$begingroup$


I'm working at classifying documents according to their content.



First I built a decision tree model that gives 90% of goods results.



Then I tried a TFIDF/SVC approach which also gives 90% of good results.



So now i'd like to combine both. My first thought was to add the prediction of TFIDF/SVC as a feature of the decision tree.



I saw this post about bagging/stacking/boosting. For me, adding the feature to the decision tree is equivalent to stacking. Is that correct ?






machine-learning decision-trees tfidf







share|improve this question









$endgroup$

















    1












    $begingroup$


    I'm working at classifying documents according to their content.



    First I built a decision tree model that gives 90% of goods results.



    Then I tried a TFIDF/SVC approach which also gives 90% of good results.



    So now i'd like to combine both. My first thought was to add the prediction of TFIDF/SVC as a feature of the decision tree.



    I saw this post about bagging/stacking/boosting. For me, adding the feature to the decision tree is equivalent to stacking. Is that correct ?






    machine-learning decision-trees tfidf







    share|improve this question









    $endgroup$















      1












      1








      1





      $begingroup$


      I'm working at classifying documents according to their content.



      First I built a decision tree model that gives 90% of goods results.



      Then I tried a TFIDF/SVC approach which also gives 90% of good results.



      So now i'd like to combine both. My first thought was to add the prediction of TFIDF/SVC as a feature of the decision tree.



      I saw this post about bagging/stacking/boosting. For me, adding the feature to the decision tree is equivalent to stacking. Is that correct ?






      machine-learning decision-trees tfidf







      share|improve this question









      $endgroup$




      I'm working at classifying documents according to their content.



      First I built a decision tree model that gives 90% of goods results.



      Then I tried a TFIDF/SVC approach which also gives 90% of good results.



      So now i'd like to combine both. My first thought was to add the prediction of TFIDF/SVC as a feature of the decision tree.



      I saw this post about bagging/stacking/boosting. For me, adding the feature to the decision tree is equivalent to stacking. Is that correct ?






      machine-learning decision-trees tfidf




      machine-learning decision-trees tfidf






      share|improve this question













      share|improve this question











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      share|improve this question










      asked 13 hours ago









      BertrandBertrand

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      524






















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          $begingroup$

          Yes. Stacking is essentially feeding the predictions of the base learners to a meta learner. Sort of like a model of the models. Here's a good explanation of that.



          Bagging,Boosting and Stacking






          share|improve this answer









          $endgroup$













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            active

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            $begingroup$

            Yes. Stacking is essentially feeding the predictions of the base learners to a meta learner. Sort of like a model of the models. Here's a good explanation of that.



            Bagging,Boosting and Stacking






            share|improve this answer









            $endgroup$


















              1












              $begingroup$

              Yes. Stacking is essentially feeding the predictions of the base learners to a meta learner. Sort of like a model of the models. Here's a good explanation of that.



              Bagging,Boosting and Stacking






              share|improve this answer









              $endgroup$
















                1












                1








                1





                $begingroup$

                Yes. Stacking is essentially feeding the predictions of the base learners to a meta learner. Sort of like a model of the models. Here's a good explanation of that.



                Bagging,Boosting and Stacking






                share|improve this answer









                $endgroup$



                Yes. Stacking is essentially feeding the predictions of the base learners to a meta learner. Sort of like a model of the models. Here's a good explanation of that.



                Bagging,Boosting and Stacking







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 13 hours ago









                DannyDanny

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