Validation of binary classifiers and data used to develop them
Probatus is a python package that helps validate binary classification models and the data used to develop them. Main features:
- probatus.interpret provides shap-based model interpretation tools
- probatus.metric_volatility provides tools using bootstrapping and/or different random seeds to assess metric volatility/stability.
- probatus.sample_similarity to compare two datasets using resemblance modelling, f.e.
- probatus.feature_elimination.ShapRFECV provides cross-validated Recursive Feature Elimination using shap feature importance.
- probatus.missing_values compares performance gains of different missing values imputation strategies for a given model.
pip install probatus
Documentation at ing-bank.github.io/probatus/.
You can also check out blog posts about Probatus:
- Open-sourcing ShapRFECV — Improved feature selection powered by SHAP.
- Model Explainability — How to choose the right tool?
To learn more about making a contribution to probatus, please see
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