PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
This provides a practical solution for practitioners needing interpretable ML development and diagnostics, but it is incremental as it integrates existing methods into a toolbox.
The authors tackled the challenge of developing and diagnosing interpretable machine learning models by introducing PiML, an open-access Python toolbox that supports interpretable models and model-agnostic tools, resulting in a comprehensive suite for workflows including data pipeline, training, and diagnostics with applications in banking.
PiML (read $π$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training and tuning, model interpretation and explanation, and model diagnostics and comparison. The toolbox supports a growing list of interpretable models (e.g. GAM, GAMI-Net, XGB1/XGB2) with inherent local and/or global interpretability. It also supports model-agnostic explainability tools (e.g. PFI, PDP, LIME, SHAP) and a powerful suite of model-agnostic diagnostics (e.g. weakness, reliability, robustness, resilience, fairness). Integration of PiML models and tests to existing MLOps platforms for quality assurance are enabled by flexible high-code APIs. Furthermore, PiML toolbox comes with a comprehensive user guide and hands-on examples, including the applications for model development and validation in banking. The project is available at https://github.com/SelfExplainML/PiML-Toolbox.