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  • SDMetrics
  • Getting Started
    • Installation
    • Quickstart
    • Metadata
      • Single Table Metadata
      • Multi Table Metadata
      • Sequential Metadata
  • Reports
    • Quality Report
      • What's included?
      • Single Table API
      • Multi Table API
    • Diagnostic Report
      • What's included?
      • Single Table API
      • Multi Table API
    • Other Reports
    • Visualization Utilities
  • Metrics
    • Diagnostic Metrics
      • BoundaryAdherence
      • CardinalityBoundaryAdherence
      • CategoryAdherence
      • KeyUniqueness
      • ReferentialIntegrity
      • TableStructure
    • Quality Metrics
      • CardinalityShapeSimilarity
      • CategoryCoverage
      • ContingencySimilarity
      • CorrelationSimilarity
      • KSComplement
      • MissingValueSimilarity
      • RangeCoverage
      • SequenceLengthSimilarity
      • StatisticMSAS
      • StatisticSimilarity
      • TVComplement
    • Privacy Metrics
      • DCRBaselineProtection
      • DCROverfittingProtection
      • DisclosureProtection
      • DisclosureProtectionEstimate
      • CategoricalCAP
    • ML Augmentation Metrics
      • BinaryClassifierPrecisionEfficacy
      • BinaryClassifierRecallEfficacy
    • Metrics in Beta
      • CSTest
      • Data Likelihood
        • BNLikelihood
        • BNLogLikelihood
        • GMLikelihood
      • Detection: Sequential
      • Detection: Single Table
      • InterRowMSAS
      • ML Efficacy: Sequential
      • ML Efficacy: Single Table
        • Binary Classification
        • Multiclass Classification
        • Regression
      • NewRowSynthesis
      • * OutlierCoverage
      • Privacy Against Inference
      • * SmoothnessSimilarity
  • Resources
    • Citation
    • Contributions
      • Defining your metric
      • Development
      • Release FAQs
    • Enterprise
      • Domain Specific Reports
    • Blog
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  1. Resources

Contributions

The SDMetrics library welcomes contributions for new metrics!

Benefits of contribution. SDMetrics is the largest, standalone library for synthetic data evaluation. With over 400K downloads (and counting!), your metric will reach a wide audience on its way to greater adoption by the synthetic data community.

SDMetrics Values

The SDMetrics library counts fairness, accuracy and explainability as our top values.

Fairness

SDMetrics is a model-agnostic library. Anyone who has real and synthetic data can use the library, no matter where or how the synthetic data was generated.

Accuracy

The SDMetrics library is verified by thousands of users across the globe. The core maintenance team includes MIT alums and AI researchers, here to ensure that metrics and reports can be trusted.

Explainability

SDMetrics is moving towards intuitive, easily explainable metrics and reports. With years of experience in deployable machine learning systems, we understand that clear communication of simple metrics can make or break your synthetic data project.

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Last updated 2 months ago