{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"SDMetrics evaluates synthetic data by computing quality and privacy metrics and generating comparison reports against real data, independent of how the synthetic data was created.","skillfed_tags":["synthetic-data","data-quality","evaluation-metrics"],"use_cases":["Generate a quality report comparing synthetic and real data to validate a synthetic data generation pipeline before deployment.","Compute individual metrics like BoundaryAdherence or NewRowSynthesis to audit specific synthetic data properties.","Save and share evaluation reports with stakeholders to document synthetic data fitness for downstream use.","Benchmark multiple synthetic data generation models by running the same metrics across their outputs.","Assess privacy risk by measuring how closely synthetic data reproduces real data patterns."],"what_it_does":"SDMetrics is a library for evaluating synthetic data by comparing it to the original real data. It computes metrics across multiple dimensions\u2014column shapes, pair trends, boundary adherence, row novelty\u2014and packages them into interactive reports with visualizations. The library is model-agnostic: it works with any synthetic data regardless of the generation method, asking only for the real data, synthetic data, and metadata describing the schema.\n\nThe package sits within the Synthetic Data Vault ecosystem and is designed for teams that need to measure and communicate data quality and privacy. It includes both high-level report generators (QualityReport) and granular metric functions you can call directly. Reports can be saved, loaded, and shared; visualizations are built on Plotly.","worth_installing":"Yes. SDMetrics is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and solves a concrete problem in synthetic data workflows. Install it if you generate or consume synthetic data and need to measure quality and privacy. The low install friction and model-agnostic design make it a practical fit for most data teams."},"id":"sdmetrics","links":{"html":"https://skillfed.io/packages/sdmetrics","md":"https://skillfed.io/packages/sdmetrics.md","pypi":"https://pypi.org/project/sdmetrics/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-24","license_spdx":"MIT","license_treatment":"permissive","name":"sdmetrics","python_support":"supports_current","summary":"Metrics for Synthetic Data Generation Projects"},"popularity":{"monthly_downloads":134769,"position":11461,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.28.2"}