Adding recommendations to the output of a classification model












2












$begingroup$


I have built a binary classification model using:




  • logit

  • decision trees

  • random forest

  • bagging classifier

  • gradientboost

  • xgboost

  • adaboost


I have evaluated the above models and chose xgboost based on training/test and validation metrics (accuracy, prediction, recall, f1 and AUC).



I want to now productionalize it and share the output with the business. The output would basically have a list of items with the predicted class and that could be filtered based on business needs.



However, Instead of simply giving the business the predicted classes, I want to add insights/recommendations as to why a specific item was predicted with class X and how you could go about working on the item to change its class from say X to Y.



How do I go about this? I thought of using feature importance, but my input data shape is [800,000 * 1,050] and I am not sure if it would the best way to proceed.



Are there any existing industry standard methodologies that can add interpretability to such models and convert them from a black box models to prescriptive models?






machine-learning python classification data-science-model







share|improve this question











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bumped to the homepage by Community♦ 9 mins ago


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  • $begingroup$
    If the features are sparse, you might want to simply use standardized coefficients from logistic regression, or select some individual decision trees. Explaining how can the label change is however a lot more problematic, and an optimization problem of its own. Generally speaking, if you need explanatory ability, you'll have to move away from black-box models.
    $endgroup$
    – anymous.asker
    Nov 17 '18 at 15:05
















2












$begingroup$


I have built a binary classification model using:




  • logit

  • decision trees

  • random forest

  • bagging classifier

  • gradientboost

  • xgboost

  • adaboost


I have evaluated the above models and chose xgboost based on training/test and validation metrics (accuracy, prediction, recall, f1 and AUC).



I want to now productionalize it and share the output with the business. The output would basically have a list of items with the predicted class and that could be filtered based on business needs.



However, Instead of simply giving the business the predicted classes, I want to add insights/recommendations as to why a specific item was predicted with class X and how you could go about working on the item to change its class from say X to Y.



How do I go about this? I thought of using feature importance, but my input data shape is [800,000 * 1,050] and I am not sure if it would the best way to proceed.



Are there any existing industry standard methodologies that can add interpretability to such models and convert them from a black box models to prescriptive models?






machine-learning python classification data-science-model







share|improve this question











$endgroup$




bumped to the homepage by Community♦ 9 mins ago


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















  • $begingroup$
    If the features are sparse, you might want to simply use standardized coefficients from logistic regression, or select some individual decision trees. Explaining how can the label change is however a lot more problematic, and an optimization problem of its own. Generally speaking, if you need explanatory ability, you'll have to move away from black-box models.
    $endgroup$
    – anymous.asker
    Nov 17 '18 at 15:05














2












2








2


2



$begingroup$


I have built a binary classification model using:




  • logit

  • decision trees

  • random forest

  • bagging classifier

  • gradientboost

  • xgboost

  • adaboost


I have evaluated the above models and chose xgboost based on training/test and validation metrics (accuracy, prediction, recall, f1 and AUC).



I want to now productionalize it and share the output with the business. The output would basically have a list of items with the predicted class and that could be filtered based on business needs.



However, Instead of simply giving the business the predicted classes, I want to add insights/recommendations as to why a specific item was predicted with class X and how you could go about working on the item to change its class from say X to Y.



How do I go about this? I thought of using feature importance, but my input data shape is [800,000 * 1,050] and I am not sure if it would the best way to proceed.



Are there any existing industry standard methodologies that can add interpretability to such models and convert them from a black box models to prescriptive models?






machine-learning python classification data-science-model







share|improve this question











$endgroup$




I have built a binary classification model using:




  • logit

  • decision trees

  • random forest

  • bagging classifier

  • gradientboost

  • xgboost

  • adaboost


I have evaluated the above models and chose xgboost based on training/test and validation metrics (accuracy, prediction, recall, f1 and AUC).



I want to now productionalize it and share the output with the business. The output would basically have a list of items with the predicted class and that could be filtered based on business needs.



However, Instead of simply giving the business the predicted classes, I want to add insights/recommendations as to why a specific item was predicted with class X and how you could go about working on the item to change its class from say X to Y.



How do I go about this? I thought of using feature importance, but my input data shape is [800,000 * 1,050] and I am not sure if it would the best way to proceed.



Are there any existing industry standard methodologies that can add interpretability to such models and convert them from a black box models to prescriptive models?






machine-learning python classification data-science-model




machine-learning python classification data-science-model






share|improve this question















share|improve this question













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edited Nov 16 '18 at 10:23







praveen

















asked Nov 15 '18 at 12:19









praveenpraveen

1212




1212





bumped to the homepage by Community♦ 9 mins 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♦ 9 mins ago


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














  • $begingroup$
    If the features are sparse, you might want to simply use standardized coefficients from logistic regression, or select some individual decision trees. Explaining how can the label change is however a lot more problematic, and an optimization problem of its own. Generally speaking, if you need explanatory ability, you'll have to move away from black-box models.
    $endgroup$
    – anymous.asker
    Nov 17 '18 at 15:05


















  • $begingroup$
    If the features are sparse, you might want to simply use standardized coefficients from logistic regression, or select some individual decision trees. Explaining how can the label change is however a lot more problematic, and an optimization problem of its own. Generally speaking, if you need explanatory ability, you'll have to move away from black-box models.
    $endgroup$
    – anymous.asker
    Nov 17 '18 at 15:05
















$begingroup$
If the features are sparse, you might want to simply use standardized coefficients from logistic regression, or select some individual decision trees. Explaining how can the label change is however a lot more problematic, and an optimization problem of its own. Generally speaking, if you need explanatory ability, you'll have to move away from black-box models.
$endgroup$
– anymous.asker
Nov 17 '18 at 15:05




$begingroup$
If the features are sparse, you might want to simply use standardized coefficients from logistic regression, or select some individual decision trees. Explaining how can the label change is however a lot more problematic, and an optimization problem of its own. Generally speaking, if you need explanatory ability, you'll have to move away from black-box models.
$endgroup$
– anymous.asker
Nov 17 '18 at 15:05










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      a link. This is a link where someone has answered a similar question like that of yours. Have a read to see if it helps.






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        a link. This is a link where someone has answered a similar question like that of yours. Have a read to see if it helps.







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        answered Nov 17 '18 at 22:25









        SudhiSudhi

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