You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I am trying to understand the SHAP outputs from a Zero-Adjusted Gamma. I'll outline my questions below:
SHAP scores should sum to the models predictions. In the LSS context for a ZAG, they should sum to the concentration, rate, and gate shape parameters that are output from predict(..., pred_type="parameters"), correct? I find this is not quite the case when I look at the outputs of shap.TreeExplainer(). So I assume there is some transformation happening. Could someone elaborate on what those transformations are, and what they look like.
This one is maybe more general, but just in terms of interpreting the SHAP values for the shape distribution parameters, in the context of the transformations. If an observation's feature has a negative SHAP score for the gate parameter, that would imply it is, on average, decreasing the probability of a 0 outcome, and a positive SHAP score would imply it is increasing the probability of a 0 outcome. Is that interpretation correct?
In a normal Gamma distribution, the mean can be described by multiplying concentration and rate (I think). Can multiplying the SHAP scores for concentration and rate be used as a high level descriptor of the predictors effects on the non-zero mean part of the distribution? And, how does the transformations affect the results of that?
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
-
I am trying to understand the SHAP outputs from a Zero-Adjusted Gamma. I'll outline my questions below:
concentration
,rate
, andgate
shape parameters that are output frompredict(..., pred_type="parameters")
, correct? I find this is not quite the case when I look at the outputs ofshap.TreeExplainer()
. So I assume there is some transformation happening. Could someone elaborate on what those transformations are, and what they look like.gate
parameter, that would imply it is, on average, decreasing the probability of a 0 outcome, and a positive SHAP score would imply it is increasing the probability of a 0 outcome. Is that interpretation correct?concentration
andrate
(I think). Can multiplying the SHAP scores forconcentration
andrate
be used as a high level descriptor of the predictors effects on the non-zero mean part of the distribution? And, how does the transformations affect the results of that?Thank you!
Beta Was this translation helpful? Give feedback.
All reactions