How to measure variable contribution to an observation in a non-linear model?
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Based on my model, if I decline someone due to their score, it should be able to provide some reasoning as to which variables mainly contributed to the decision to decline.
Typically in Logistic Regression models, this is a simple exercise where you calculate (Beta * X) for each variable and pick 1 or 2 variables which caused the biggest score drop.
However, this isn't very straightforward for non-linear models. I would appreciate any ideas on handling something like this. Thanks.
machine-learning score collinearity
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
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bumped to the homepage by Community♦ 18 mins ago
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add a comment |
$begingroup$
Based on my model, if I decline someone due to their score, it should be able to provide some reasoning as to which variables mainly contributed to the decision to decline.
Typically in Logistic Regression models, this is a simple exercise where you calculate (Beta * X) for each variable and pick 1 or 2 variables which caused the biggest score drop.
However, this isn't very straightforward for non-linear models. I would appreciate any ideas on handling something like this. Thanks.
machine-learning score collinearity
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
$endgroup$
bumped to the homepage by Community♦ 18 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 |
$begingroup$
Based on my model, if I decline someone due to their score, it should be able to provide some reasoning as to which variables mainly contributed to the decision to decline.
Typically in Logistic Regression models, this is a simple exercise where you calculate (Beta * X) for each variable and pick 1 or 2 variables which caused the biggest score drop.
However, this isn't very straightforward for non-linear models. I would appreciate any ideas on handling something like this. Thanks.
machine-learning score collinearity
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
$endgroup$
Based on my model, if I decline someone due to their score, it should be able to provide some reasoning as to which variables mainly contributed to the decision to decline.
Typically in Logistic Regression models, this is a simple exercise where you calculate (Beta * X) for each variable and pick 1 or 2 variables which caused the biggest score drop.
However, this isn't very straightforward for non-linear models. I would appreciate any ideas on handling something like this. Thanks.
machine-learning score collinearity
machine-learning score collinearity
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
asked Jan 16 at 16:55
rayven1lkrayven1lk
466
466
bumped to the homepage by Community♦ 18 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♦ 18 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 |
add a comment |
1 Answer
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Try LIME. In other words, compute local representation of your global highly nonlinear model for a given observation. This local proxy should be simple enough to be explainable as well. Decision trees or simple linear regressions are good choices.
answered Jan 17 at 3:09
user2280549user2280549
1334
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1 Answer
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1 Answer
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$begingroup$
Try LIME. In other words, compute local representation of your global highly nonlinear model for a given observation. This local proxy should be simple enough to be explainable as well. Decision trees or simple linear regressions are good choices.
answered Jan 17 at 3:09
user2280549user2280549
1334
$endgroup$
add a comment |
$begingroup$
Try LIME. In other words, compute local representation of your global highly nonlinear model for a given observation. This local proxy should be simple enough to be explainable as well. Decision trees or simple linear regressions are good choices.
answered Jan 17 at 3:09
user2280549user2280549
1334
$endgroup$
add a comment |
$begingroup$
Try LIME. In other words, compute local representation of your global highly nonlinear model for a given observation. This local proxy should be simple enough to be explainable as well. Decision trees or simple linear regressions are good choices.
answered Jan 17 at 3:09
user2280549user2280549
1334
$endgroup$
Try LIME. In other words, compute local representation of your global highly nonlinear model for a given observation. This local proxy should be simple enough to be explainable as well. Decision trees or simple linear regressions are good choices.
answered Jan 17 at 3:09
user2280549user2280549
1334
answered Jan 17 at 3:09
user2280549user2280549
1334
answered Jan 17 at 3:09
user2280549user2280549
1334
answered Jan 17 at 3:09
user2280549user2280549
1334
1334
add a comment |
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