interpretml/interpret
interpret
Fit interpretable models. Explain blackbox machine learning.
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interpretml
interpretml • individual
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Created
May 3, 2019
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README Summary
<a href="https://githubtocolab.com/interpretml/interpret/blob/main/docs/interpret/python/examples/interpretable-classification.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> [](https://mybinder.org/v2/gh/interpretml/interpret/main?labpath=docs%2Finterpret%2Fpython%2Fexamples%2Finterpretable-classification.ipynb) Feature Importance AnalysisGeneralized Additive ModelsGeneralized Additive Models (GAM)Glasbox Model DesignInteraction DetectionInteractive Visualization of Model DecisionsLIME ImplementationLIME TechniquesLinear ModelsModel-agnostic Explanation MethodsModel-Agnostic Explanation MethodsModel InterpretabilityPartial Dependence PlotsRule-based LearningSHAP IntegrationTransparency in Machine LearningTree-based Model Interpretation
Tags
Additive ModelsBlack-box Model ExplanationConcept-based ExplanationsDecision Tree EnsemblesDecision Trees and Rule-based ModelsEnsemble MethodsExplainable AI (XAI)Feature Importance AnalysisGeneralized Additive ModelsGeneralized Additive Models (GAM)Glasbox Model DesignInteraction DetectionInteractive Visualization of Model DecisionsLIME ImplementationLIME TechniquesLinear ModelsModel-agnostic Explanation MethodsModel-Agnostic Explanation MethodsModel InterpretabilityPartial Dependence PlotsRule-based LearningSHAP IntegrationTransparency in Machine LearningTree-based Model InterpretationBenchmarkingData ScienceDeep LearningDistillationEmbeddingsEvalsFinTechForkedHealthcare AIJupyterMachine LearningModel OptimizationNumPyONNXOpen SourcePandasPrivacyPrivacy-Preserving AIPythonReal-Time / StreamingResearch / PapersScikit-learnSecurityStatisticsTransformersTutorial
Taxonomy
AI Trends
category
Deployment Context
Industries
Skill Areas
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
tag
Use Cases
Credit Risk Assessment with ExplainabilityMedical Diagnosis ExplanationRegulatory Model Audit and ComplianceFeature Contribution AnalysisModel Decision TransparencyBlack-box Model InterpretationStakeholder Communication of Model DecisionsModel transparency for regulatory complianceFeature importance rankingBlack-box model explanationInherently interpretable model trainingRisk assessment and creditworthiness evaluationMedical diagnosis explanationModel debugging and validationModel Debugging and ValidationRegulatory Compliance and Audit TrailsFeature Selection and EngineeringBlackbox Model ExplanationDecision Tree ExtractionCredit Scoring TransparencyRisk Factor Analysis
Recent Activity
Updated 2 months ago
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Quality
production- Quality
- high
- Maturity
- production
Categories
Data Science & AnalyticsPrimaryRAG & RetrievalEvals & BenchmarkingInference & ServingDev Tools & AutomationLearning ResourcesIndustry: HealthcareIndustry: FinTechSecurity & SafetyFoundation ModelsHealthcare & BiologyFinance & LegalEdge & Mobile AISearch & KnowledgeOther AI / ML
PM Skills
Safety & AlignmentScale & ReliabilityData & EvaluationProduct Discovery
Languages
C++100.0%
Timeline
- Project created
- May 3, 2019
- Forked
- Mar 29, 2026
- Your last push
- 2 months ago
- Upstream last push
- 18 days ago
- Tracked since
- Mar 26, 2026
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