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After investigating, I am sure that the root of the problem is with the eli5 library. See this issue. I can't say for sure if the developers of that package will ever update it so that it works with newer versions of scikit-learn, so maybe a work-around is needed for scalecast. I'm not sure what that would be as scikit-learn 1.3.1 is needed to do some things in scalecast. If you need feature importance while I try to figure something out for this, you can try setting method = 'shap' when using feature importance, but I believe it only works for tree-based models right now.
- Added more feature importance options, all sourced through the shap library.
- shap is now a requirement and eli5 is not.
- Changed `Forecaster.reduce_Xvars()` to use only shap feature importance to rank features.
- Removed `fi_method` argument from `tune_test_forecast()`.
- Fixed how a pandas function was called that was raising a warning.
- Fixed feature importance to use shap only with TreeExplainer, PermutationExplainer, and other explainers (#85). See the [docs](https://scalecast.readthedocs.io/en/latest/Forecaster/Forecaster.html#src.scalecast.Forecaster.Forecaster.save_feature_importance) The eli5 package appears to be deprecated.
The fix for this is in 0.19.4. See the new save_feature_importance() documentation. Feature importance has been expanded in the package to include the two types previously offered, plus five additional methods, all through the shap package.
If you agree with the fix, we will close the issue.
Hello,
Thank you for your development and support of this valuable package.
I cannot get
feature_importance=True
andsummary_stats=True
to behave as expected. All models seem to be affected.I ran --upgrade yesterday.
Environment:
My forecaster objects are generated by:
tune_test_forecast
is looped with:The warnings/errors I get are below. Prophet verbose INFO has been removed:
I would expect FI for most of these sklearn models. Can you please help me understand this miss?
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