SelfExplainML/PiML-Toolbox

Health v2 · maintenance only

Adoption not part of the score

1285 stars · 135 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PiML is a Python toolbox for developing, diagnosing, and validating inherently interpretable machine learning models such as GLMs, GAMs, decision trees, EBM, GAMI-Net, XGB1/XGB2, and ReLU-DNNs, and it also wraps arbitrary black-box supervised models. It provides both a low-code graphical interface and high-code APIs covering the full model development and validation workflow, including data handling, fitting, explainability, robustness analysis, and interactive diagnostic visualizations.

Use cases

• build interpretable machine learning models in python
• explain and diagnose ml model predictions
• validate black-box machine learning models for regulatory model risk management
• fit glm and gam models with built-in diagnostics
• low-code machine learning model development
• analyze model robustness and failure modes
• visualize feature effects and model interpretability

When to choose

• you need inherently interpretable models (GLM, GAM, EBM, GAMI-Net, ReLU-DNN) for regulated or high-stakes decision-making
• you want one integrated workflow combining low-code UI and Python APIs for model development and validation
• you need rich diagnostic visualizations such as residual analysis, fairness, robustness, and explainability plots for tabular regression or binary classification

When to avoid

• you are starting a new project - the authors have moved to its successor MoDeVa (modeva.ai) as of March 2025, so PiML now only receives maintenance
• you need deep learning, NLP, or LLM workflows rather than interpretable tabular supervised learning
• you only care about maximizing predictive accuracy with black-box models and have no interpretability or diagnostic requirements

Facets

library · maturity maintenance

machine-learning data-science data-visualization monitoring machine-learning data-science data-visualization artificial-intelligence fintech python cross-platform interpretable-machine-learning explainable-ai model-diagnostics model-validation low-code glassbox-models model-development xai feature-effect-analysis model-risk-management

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