Comments (3)
CMobley7
commented on June 6, 2024
1
It looks like the folds param could be added nicely to this function: https://github.com/ThomasBury/arfs/blob/main/src/arfs/feature_selection/allrelevant.py#L2188. Also, using fasttreeshap already defaults to false. Would you accept a PR that did these two things?
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ThomasBury
commented on June 6, 2024
Hello @CMobley7,
I plan to examine the latest version of Optuna to determine if it now supports the most recent LightGBM version. As you mentioned, FastTreeShap currently does not support the latest LightGBM version and encounters additive check errors, which means it cannot accurately match the target value using the SHAP linear approximation.
You can explore the GPU option at https://arfs.readthedocs.io/en/latest/notebooks/arfs_on_GPU.html.
Regarding the folds, while it shouldn't be too difficult, it will still require some effort and refactoring. I'll dedicate time to this task when I have the opportunity.
Best regards.
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ThomasBury
commented on June 6, 2024
The folds can now be user-defined 3771d49
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Related Issues (20)
- Leshy works wrong with categorical features HOT 2
- potential to specify time series splitter HOT 7
- GrootCV is missing class_weight param for muticlass classification HOT 1
- Numba HOT 1
- Consider using FastTreeSHAP? HOT 5
- Ability to pass in a model to GrootCV HOT 7
- arfs.feature_selection module not found HOT 4
- Cannot suppress runtime warning HOT 1
- [BUG] - add a safeguard when there is a single categorical column
- [BUG] User-Specified Threshold for CollinearityThreshold is not Applied. HOT 1
- Leshy fit method always overwrites to importance==shap if fasttreeshap not installed HOT 3
- Issue with Custom Callable Implementation in CollinearityThreshold Class HOT 2
- Issue with Overly Aggressive Feature Removal in CollinearityThreshold Class
- Bug: MinRedundancyMaxRelevance Function Modifies Input DataFrame by Adding target Column HOT 2
- Possible bugs in `CollinearityThreshold` HOT 9
- CollinearityThreshold has the wrong default
- Duplicated feature importance columns in reduce_vars_sklearn HOT 2
- Max on the wrong axis in _reduce_vars_sklearn HOT 3
- Feature Selection Accuracy Comparison
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