emphasise some observation weights more than the others












0












$begingroup$


I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.



In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.



Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?






weighted-data







share|improve this question









$endgroup$




bumped to the homepage by Community♦ 19 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.











  • 2




    $begingroup$
    Are you sure it does that? The documentation suggests otherwise; see sample_weight_last_ten.
    $endgroup$
    – Emre
    Apr 16 '18 at 20:34


















0












$begingroup$


I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.



In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.



Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?






weighted-data







share|improve this question









$endgroup$




bumped to the homepage by Community♦ 19 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.











  • 2




    $begingroup$
    Are you sure it does that? The documentation suggests otherwise; see sample_weight_last_ten.
    $endgroup$
    – Emre
    Apr 16 '18 at 20:34
















0












0








0





$begingroup$


I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.



In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.



Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?






weighted-data







share|improve this question









$endgroup$




I want to emphasise (increase the weight) of only a subset of data. Lets say I have old and fresh data, I would like to say that old data has to have more weight and therefore has more influence in the decision than the new data.



In scikit-learn I found only class-weight parameter, but it does not change the weight of the samples, only of all samples within the class.



Is there a way to incorporate this emphasis into the gradient boosted trees in spark or xgboost in python?






weighted-data




weighted-data






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Apr 16 '18 at 11:45









TonjaTonja

1033




1033





bumped to the homepage by Community♦ 19 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







bumped to the homepage by Community♦ 19 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.










  • 2




    $begingroup$
    Are you sure it does that? The documentation suggests otherwise; see sample_weight_last_ten.
    $endgroup$
    – Emre
    Apr 16 '18 at 20:34
















  • 2




    $begingroup$
    Are you sure it does that? The documentation suggests otherwise; see sample_weight_last_ten.
    $endgroup$
    – Emre
    Apr 16 '18 at 20:34










2




2




$begingroup$
Are you sure it does that? The documentation suggests otherwise; see sample_weight_last_ten.
$endgroup$
– Emre
Apr 16 '18 at 20:34






$begingroup$
Are you sure it does that? The documentation suggests otherwise; see sample_weight_last_ten.
$endgroup$
– Emre
Apr 16 '18 at 20:34












2 Answers
2






active

oldest

votes


















1












$begingroup$

If you have a date variable (or something similar), you can create a weight using this.



If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.






share|improve this answer









$endgroup$





















    0












    $begingroup$

    There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:




    1. A1

    2. A2

    3. B1

    4. B2

    5. C1

    6. C2


    add:




    1. C1

    2. C2






    share|improve this answer









    $endgroup$









    • 1




      $begingroup$
      You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
      $endgroup$
      – bradS
      May 17 '18 at 8:00












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    2 Answers
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    active

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    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1












    $begingroup$

    If you have a date variable (or something similar), you can create a weight using this.



    If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.






    share|improve this answer









    $endgroup$


















      1












      $begingroup$

      If you have a date variable (or something similar), you can create a weight using this.



      If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.






      share|improve this answer









      $endgroup$
















        1












        1








        1





        $begingroup$

        If you have a date variable (or something similar), you can create a weight using this.



        If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.






        share|improve this answer









        $endgroup$



        If you have a date variable (or something similar), you can create a weight using this.



        If you're using XGBoost, there is an option to specify a weight for each instance when creating the DMatrix - feed your observation weighting in here.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered May 17 '18 at 8:02









        bradSbradS

        667213




        667213























            0












            $begingroup$

            There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:




            1. A1

            2. A2

            3. B1

            4. B2

            5. C1

            6. C2


            add:




            1. C1

            2. C2






            share|improve this answer









            $endgroup$









            • 1




              $begingroup$
              You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
              $endgroup$
              – bradS
              May 17 '18 at 8:00
















            0












            $begingroup$

            There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:




            1. A1

            2. A2

            3. B1

            4. B2

            5. C1

            6. C2


            add:




            1. C1

            2. C2






            share|improve this answer









            $endgroup$









            • 1




              $begingroup$
              You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
              $endgroup$
              – bradS
              May 17 '18 at 8:00














            0












            0








            0





            $begingroup$

            There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:




            1. A1

            2. A2

            3. B1

            4. B2

            5. C1

            6. C2


            add:




            1. C1

            2. C2






            share|improve this answer









            $endgroup$



            There might be a fancier way to create dynamic weights but I would probably start with oversampling the subset and see how that goes. So if you've got classes A, B, and C and want to emphasize C, make a duplicate copy of C and insert that into your training data. In other words, assume you have six records to train on:




            1. A1

            2. A2

            3. B1

            4. B2

            5. C1

            6. C2


            add:




            1. C1

            2. C2







            share|improve this answer












            share|improve this answer



            share|improve this answer










            answered Apr 16 '18 at 18:51









            CalZCalZ

            1,438213




            1,438213








            • 1




              $begingroup$
              You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
              $endgroup$
              – bradS
              May 17 '18 at 8:00














            • 1




              $begingroup$
              You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
              $endgroup$
              – bradS
              May 17 '18 at 8:00








            1




            1




            $begingroup$
            You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
            $endgroup$
            – bradS
            May 17 '18 at 8:00




            $begingroup$
            You should be very wary of this method as it may introduce biases and skewness into your data - e.g. the distribution of certain variables may change.
            $endgroup$
            – bradS
            May 17 '18 at 8:00


















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