mlsanityData to 14 Oct 2026

PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics

arXiv 2305.04214 ↗First posted 7 May 2023cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 7 May 2023.

Builds on

15 of 32 references.

References · 15

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2020Captum: A unified and generic model interpretability library for PyTorchcited by 547
  4. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 246
  5. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 213
  6. 2020GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionscited by 82
  7. 2020General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Modelscited by 75
  8. 2018Automated Data Slicing for Model Validation:A Big data - AI Integration Approachcited by 70
  9. 2019Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Modelscited by 42
  10. 2020dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Pythoncited by 40
  11. 2020Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplificationcited by 32
  12. 2019Enhancing Explainability of Neural Networks through Architecture Constraintscited by 29
3 more
  1. 2021Designing Inherently Interpretable Machine Learning Modelscited by 23
  2. 2020Optimal binning: mathematical programming formulationcited by 9
  3. 2023Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularizationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classificationcited by 2
  2. 2025dnamite: A Python Package for Neural Additive Modelscited by 0
  3. 2026Woodelf++: A Fast and Unified Partial Dependence Plot Algorithm for Decision Tree Ensemblescited by 0
  4. 2026K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Datacited by 0