“This is based on the idea that when all features are on the same scale, the most important features should have the highest coefficients in the model, while features uncorrelated with the output variables should have coefficient values close to zero.”

]]>Thanks for all the great work.

I noted that ExtraTreesClassifier models will work in the readme, but when one is supplied, it triggers the value error looking for a DTclassifier or DT regressor.

Are the ExtraTreesClassifier models not yet supported?

Thanks!

]]>I have seen a similar implementation in R (xgboostExplainer, on CRAN). The main difference is that contributions are expressed in log-odds of probability.

I’m curious about your thoughts of using log-odds, which has the advantage to bring a “bayesian interpretation” of contributions. However, it seems that it is not possible to maintain all additivity properties [1] and [2] ([1] a contribution of feature F is equal to the mean of the contributions of feature F for all decision trees ; [2] the prediction score is equal to the sum of all feature contributions and equal to the mean of prediction score for all decision trees.).

Any thoughts on using log-odds for contributions?

The calculation of all features could be too time consuming. ]]>