Library/interpretForked

interpretml/interpret

interpret

Fit interpretable models. Explain blackbox machine learning.

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Builder

interpretml

interpretml • individual

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6,823

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Forks

783

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0/100

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Created

May 3, 2019

Project creation date

README Summary

InterpretML is a unified framework for machine learning interpretability that allows users to fit glass-box models that are inherently interpretable and explain black-box models through various explanation techniques. The library provides a consistent API for both global and local explanations across different model types and includes state-of-the-art algorithms like Explainable Boosting Machines (EBM).

AI Dev Skills

Unmapped

Model InterpretabilityExplainable AI (XAI)Feature Importance AnalysisBlack-box Model ExplanationGeneralized Additive ModelsDecision Tree EnsemblesGlasbox Model DesignModel-agnostic Explanation MethodsInteractive Visualization of Model DecisionsTransparency in Machine LearningModel-Agnostic Explanation MethodsAdditive ModelsGeneralized Additive Models (GAM)Ensemble MethodsDecision Trees and Rule-based ModelsSHAP IntegrationLIME TechniquesPartial Dependence PlotsInteraction DetectionLinear ModelsTree-based Model InterpretationLIME ImplementationRule-based LearningConcept-based Explanations

Taxonomy

AI Trends

Explainable AI AI Safety Regulatory AI Compliance Interpretable Machine Learning Trustworthy AI AI Transparency Responsible AI AI Governance Model Debugging

Deployment Context

Self-hosted On-premise Cloud API

Industries

FinTech Healthcare Regulatory Compliance Risk Management Legal Tech Finance Insurance Compliance and Regulatory

Modalities

Tabular Text

Skill Areas

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Recent Activity

Updated 18 days ago

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Quality

production

Quality: high
Maturity: production

PM Skills

Developer Platform

Languages

C++100.0%

Timeline

Project created: May 3, 2019
Forked: Mar 29, 2026
Your last push: 18 days ago
Upstream last push: 18 days ago
Tracked since: Mar 26, 2026

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