xRiskLab/xBooster

Fit interpretable models. Explain blackbox machine learning.

A game theoretic approach to explain the output of any machine learning model.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world datasets and workflows.

Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

skops is a Python library helping you share your scikit-learn based models and put them in production

scorecardpipeline封装常用的风控策略分析和评分卡建模相关组件，支持pipeline式端到端评分卡建模、三方数据分析、规则集效果评估、特征有效性分析、excel报告输出、评分卡PMML导出、全流程超参数搜索等功能。核心功能：评分卡，策略分析，风控，规则挖掘，特征筛选，自动分箱

Calibrated Explanations is an explanation method for uncertainty-aware explanations of classification and regression models.