Comments (1)
There is nothing in our current repo specifically designed for dealing with time series. However, if you can use standard supervised learning for your time series prediction, then the EBMs should work well and remain interpretable.
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Related Issues (20)
- Development installation: Requirements? HOT 2
- Query: performance prospects on massive data sets (curse of dimensionality?) HOT 3
- How to speed up EBM model? Unbelievable slow. HOT 9
- Question: Parallel boosting? HOT 4
- Integrate EBM into the pytorch framework HOT 7
- Visualising Decision Tree explainer gives a Cytoscape object which is not savable to my local machine HOT 2
- [DP-EBM] Question regarding range R and sensitivity
- Support for more parameters in the Differentially Private models HOT 1
- NAM Model HOT 1
- Some hyperparameter questions HOT 3
- Lookup Table for single feature and feature interaction terms HOT 4
- Operations when merging EBM HOT 6
- EBM Classifier Global Feature Importance x Random Forest Classifier with Morris Sensitivity Analysis HOT 1
- possibility of adding `sample_weight` to `interpret.glassbox.ClassificationTree` HOT 6
- 2d PDP Z-axis colours appear too similar HOT 1
- Exporting EBM as PMML HOT 3
- Feature Request: Passing Validation Set or Index HOT 2
- Explore the data with continuous output and category input HOT 4
- Using the init_score in EBM Classifier HOT 1
- Merging two EBM regressors leads to model that has NaNs in attributes
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