Adding recommendations to the output of a classification model
$begingroup$
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
asked Nov 15 '18 at 12:19
praveenpraveen
1212
$endgroup$
bumped to the homepage by Community♦ 7 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
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
asked Nov 15 '18 at 12:19
praveenpraveen
1212
$endgroup$
bumped to the homepage by Community♦ 7 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
add a comment |
$begingroup$
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
asked Nov 15 '18 at 12:19
praveenpraveen
1212
$endgroup$
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
asked Nov 15 '18 at 12:19
praveenpraveen
1212
asked Nov 15 '18 at 12:19
praveenpraveen
1212
edited Nov 16 '18 at 10:23
praveen
asked Nov 15 '18 at 12:19
praveenpraveen
1212
asked Nov 15 '18 at 12:19
praveenpraveen
1212
asked Nov 15 '18 at 12:19
praveenpraveen
1212
1212
bumped to the homepage by Community♦ 7 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♦ 7 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
add a comment |
$begingroup$
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
add a comment |
1 Answer
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oldest
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$begingroup$
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.
answered Nov 17 '18 at 22:25
SudhiSudhi
415
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add a comment |
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1 Answer
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$begingroup$
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.
answered Nov 17 '18 at 22:25
SudhiSudhi
415
$endgroup$
add a comment |
$begingroup$
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.
answered Nov 17 '18 at 22:25
SudhiSudhi
415
$endgroup$
add a comment |
$begingroup$
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.
answered Nov 17 '18 at 22:25
SudhiSudhi
415
$endgroup$
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.
answered Nov 17 '18 at 22:25
SudhiSudhi
415
answered Nov 17 '18 at 22:25
SudhiSudhi
415
answered Nov 17 '18 at 22:25
SudhiSudhi
415
answered Nov 17 '18 at 22:25
SudhiSudhi
415
415
add a comment |
add a comment |
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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