Comments (5)
@FlorianWetschoreck I just demonstrated for selecting features for a model with xgboost vs ppscore.
you can check it here.
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Hi Matt,
I am also looking forward to some formal comparisons between feature importance and the PPS. If you created some for your personal studies, it would be much appreciated if you share them.
Some general thoughts:
- you are right that PPS only corresponds to a single feature's predictive power on the target and thus, it should be accompanied by looking at the feature importances.
- Feature Importance is sometimes misleading depending on the calculation method. Therefore, I would suggest to always use Permutation Feature Importance https://scikit-learn.org/stable/modules/permutation_importance.html
- It might be interesting to subtract the PPS from the Permutation Feature Importance in order to estimate the amount of predictive power that resulted from interactions with other features
Best,
Florian
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Great, happy to hear that!
You might also want to add the Maximal Information Coefficient into the mix:
https://medium.com/@rhondenewint93/on-maximal-information-coefficient-a-modern-approach-for-finding-associations-in-large-data-sets-ba8c36ebb96b
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Any updates from your side or did your priorities change?
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Related Issues (20)
- Data preprocessing and information leakage HOT 14
- [SUGGEST] Release a verson supported GPU HOT 2
- ppscore when model_score>baseline_score HOT 3
- There should be an option to override the attribute type like PyCaret HOT 4
- Scikit-learn dependency < 1.0.0 HOT 14
- [Suggestion]: Plot the Decision Tree for pps.score HOT 3
- Readme / docs unclear about using ppscore on time series data HOT 3
- pytests failing with pandas==1.4.0 HOT 1
- Thought on a possible enhancement of the PPS HOT 2
- What does PPS score? HOT 4
- Add support to release Linux aarch64 wheels HOT 4
- Cannot install ppscore HOT 1
- Your package isn't compatible with scikit-learn 1.0.1 HOT 2
- How to report PPS HOT 1
- Question About Data Order HOT 12
- y predicted values given x HOT 3
- Performance HOT 3
- differnt baseline scores for the same y HOT 1
- How to deal with heavy imbalanced data? For example, when the target is 99 "negative" to 1 "positive"
- pandas >2 support HOT 1
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