sklearn-expertsys
A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.
What is sklearn-expertsys?
sklearn-expertsys is a scikit-learn compatible classifier that produces highly interpretable decision rules instead of black box models. It implements Bayesian Rule Lists with extensions for continuous data discretization and large dataset optimization, solving the problem of opaque machine learning models by providing transparent, human-readable explanations for predictions.
Data scientists and machine learning practitioners who need interpretable models for domains requiring transparency, such as healthcare, finance, or regulatory compliance, where understanding model decisions is critical.
Developers choose this library because it offers competitive accuracy while providing fully interpretable models, bridging the gap between complex machine learning performance and human-understandable decision rules that domain experts can validate and trust.
Overview
Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models
Use Cases
Best For
- Building interpretable classifiers for regulated industries like healthcare or finance
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
