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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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On this page
  • Flexible, Intuitive Evaluation
  • 📊 Visualize & share your results with reports
  • ⚖️ Choose from a variety of metrics
  • 📚 Participate in cutting edge research
  • Owned & Maintained by DataCebo

SDMetrics

NextInstallation

Last updated 1 month ago

Synthetic Data Metrics (SDMetrics) is an Python library for evaluating tabular synthetic data. Compare synthetic data against real data using a variety metrics, generate visual reports and share them with your team.

Flexible, Intuitive Evaluation

The SDMetrics library is model-agnostic, meaning you can use it with synthetic data created by any model at any time.

Owned & Maintained by DataCebo

Visualize & share your results with reports

Easily generate reports for your project. Reports focus on a particular aspect of synthetic data, for example . Use them to drill down visually until you get answers.

We are also here to help with tailored to your enterprise needs.

Choose from a variety of metrics

You'll find many different types of for evaluating synthetic data. SDMetrics docs explain relevant mathematical concepts and help you decide the best ones to apply.

Participate in cutting edge research

The SDMetrics library welcomes contributions from active research areas! Browse our and experiment with cutting edge methods to evaluate your data.

The SDMetrics library is a part of the , first created at MIT's in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project.

Today, is the proud developer of the SDV, the largest ecosystem for synthetic data generation & evaluation.

📊
data quality
custom reports
⚖️
metrics
📚
Metrics in Beta
Synthetic Data Vault Project
Data to AI Lab
DataCebo
open source
This is an example a visualization from the SDMetrics .
This is an example illustrating the metric that measures privacy.
Quality Report
DisclosureProtection