PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics

• URL: http://arxiv.org/abs/2305.04214v3
• Date: Tue, 19 Dec 2023 21:02:06 GMT
• Title: PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
• Authors: Agus Sudjianto, Aijun Zhang, Zebin Yang, Yu Su, Ningzhou Zeng
• Abstract summary: PiML is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning in both low-code and high-code modes, including data pipeline, model training and tuning, model interpretation and explanation.
• Score: 10.635578367440162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: PiML (read $\pi$-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.

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