When GBM model saved and loaded doesn't give same predicted values when model loaded again and again












0















I trained a GBM model (classification) and saved it using:



saveRDS(gbmfit, file='E:/..../gbm_nb.rds')


when I am using above model for scoring data, I load it using:



gbmfit <- readRDS('E:/..../gbm_nb.rds')


and predict:



nb_lapse$PRED <- predict(gbmfit, nb_lapse, type='response', n.trees=2000)


This PRED values should be same each time I run it against same scoring data. But some of these values come different. I have printed these by two runs of above code and the differences are around 3% of records. The difference I have printed as such:
enter image description here



Where ID is the unique number. PRD_ORIG is coming in the first run and PRED_NEW is coming in the second run. In second run I am loading model again. If I am not loading model, instead score data again on already loaded model then I don't see any difference in PRED values.



Have you seen this kind of behavior? I was expecting that this should give same PRED values, for same scoring data, each time I will load model and score it.



Thanks.










share|improve this question



























    0















    I trained a GBM model (classification) and saved it using:



    saveRDS(gbmfit, file='E:/..../gbm_nb.rds')


    when I am using above model for scoring data, I load it using:



    gbmfit <- readRDS('E:/..../gbm_nb.rds')


    and predict:



    nb_lapse$PRED <- predict(gbmfit, nb_lapse, type='response', n.trees=2000)


    This PRED values should be same each time I run it against same scoring data. But some of these values come different. I have printed these by two runs of above code and the differences are around 3% of records. The difference I have printed as such:
    enter image description here



    Where ID is the unique number. PRD_ORIG is coming in the first run and PRED_NEW is coming in the second run. In second run I am loading model again. If I am not loading model, instead score data again on already loaded model then I don't see any difference in PRED values.



    Have you seen this kind of behavior? I was expecting that this should give same PRED values, for same scoring data, each time I will load model and score it.



    Thanks.










    share|improve this question

























      0












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      1






      I trained a GBM model (classification) and saved it using:



      saveRDS(gbmfit, file='E:/..../gbm_nb.rds')


      when I am using above model for scoring data, I load it using:



      gbmfit <- readRDS('E:/..../gbm_nb.rds')


      and predict:



      nb_lapse$PRED <- predict(gbmfit, nb_lapse, type='response', n.trees=2000)


      This PRED values should be same each time I run it against same scoring data. But some of these values come different. I have printed these by two runs of above code and the differences are around 3% of records. The difference I have printed as such:
      enter image description here



      Where ID is the unique number. PRD_ORIG is coming in the first run and PRED_NEW is coming in the second run. In second run I am loading model again. If I am not loading model, instead score data again on already loaded model then I don't see any difference in PRED values.



      Have you seen this kind of behavior? I was expecting that this should give same PRED values, for same scoring data, each time I will load model and score it.



      Thanks.










      share|improve this question














      I trained a GBM model (classification) and saved it using:



      saveRDS(gbmfit, file='E:/..../gbm_nb.rds')


      when I am using above model for scoring data, I load it using:



      gbmfit <- readRDS('E:/..../gbm_nb.rds')


      and predict:



      nb_lapse$PRED <- predict(gbmfit, nb_lapse, type='response', n.trees=2000)


      This PRED values should be same each time I run it against same scoring data. But some of these values come different. I have printed these by two runs of above code and the differences are around 3% of records. The difference I have printed as such:
      enter image description here



      Where ID is the unique number. PRD_ORIG is coming in the first run and PRED_NEW is coming in the second run. In second run I am loading model again. If I am not loading model, instead score data again on already loaded model then I don't see any difference in PRED values.



      Have you seen this kind of behavior? I was expecting that this should give same PRED values, for same scoring data, each time I will load model and score it.



      Thanks.







      r gbm






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      asked Nov 20 '18 at 17:22









      SanojSanoj

      4242516




      4242516
























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          I found the answer. I have one categorical variable, say A, with many categories. In the train data, I have capped those categories based on another variable, say B, in the data frame. In scoring data frame that variable B is not present. Therefore, despite my scoring data goes through capping code, actual capping of categories doesn't happen. R doesn't throw any error if code is not executed. Therefore in scoring data I end up having more categories in categorical variable A than in train data on which model was trained. This was creating problem and I was getting different predicted values for those data points where categories were different, or not present, than in train data. Once I capped scoring data categorical variable as in train data my predicted values are coming same every time.






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            active

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            I found the answer. I have one categorical variable, say A, with many categories. In the train data, I have capped those categories based on another variable, say B, in the data frame. In scoring data frame that variable B is not present. Therefore, despite my scoring data goes through capping code, actual capping of categories doesn't happen. R doesn't throw any error if code is not executed. Therefore in scoring data I end up having more categories in categorical variable A than in train data on which model was trained. This was creating problem and I was getting different predicted values for those data points where categories were different, or not present, than in train data. Once I capped scoring data categorical variable as in train data my predicted values are coming same every time.






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              I found the answer. I have one categorical variable, say A, with many categories. In the train data, I have capped those categories based on another variable, say B, in the data frame. In scoring data frame that variable B is not present. Therefore, despite my scoring data goes through capping code, actual capping of categories doesn't happen. R doesn't throw any error if code is not executed. Therefore in scoring data I end up having more categories in categorical variable A than in train data on which model was trained. This was creating problem and I was getting different predicted values for those data points where categories were different, or not present, than in train data. Once I capped scoring data categorical variable as in train data my predicted values are coming same every time.






              share|improve this answer


























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                0







                I found the answer. I have one categorical variable, say A, with many categories. In the train data, I have capped those categories based on another variable, say B, in the data frame. In scoring data frame that variable B is not present. Therefore, despite my scoring data goes through capping code, actual capping of categories doesn't happen. R doesn't throw any error if code is not executed. Therefore in scoring data I end up having more categories in categorical variable A than in train data on which model was trained. This was creating problem and I was getting different predicted values for those data points where categories were different, or not present, than in train data. Once I capped scoring data categorical variable as in train data my predicted values are coming same every time.






                share|improve this answer













                I found the answer. I have one categorical variable, say A, with many categories. In the train data, I have capped those categories based on another variable, say B, in the data frame. In scoring data frame that variable B is not present. Therefore, despite my scoring data goes through capping code, actual capping of categories doesn't happen. R doesn't throw any error if code is not executed. Therefore in scoring data I end up having more categories in categorical variable A than in train data on which model was trained. This was creating problem and I was getting different predicted values for those data points where categories were different, or not present, than in train data. Once I capped scoring data categorical variable as in train data my predicted values are coming same every time.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 20 '18 at 21:22









                SanojSanoj

                4242516




                4242516
































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