{"library":"sdmetrics","type":"library","category":null,"description":"SDMetrics is an open-source Python library developed by DataCebo (part of the Synthetic Data Vault project) for evaluating the quality and efficacy of synthetic datasets. It provides a variety of metrics to compare synthetic data against real data across aspects like quality, privacy, and utility, and includes tools for generating comprehensive visual reports. The library is model-agnostic, allowing evaluation of synthetic data generated by any model. The current version is 0.28.0, with active and frequent releases.","language":"python","status":"active","version":"0.28.0","tags":["synthetic data","data quality","metrics","evaluation","privacy","data science"],"install":[{"cmd":"pip install sdmetrics","imports":["from sdmetrics import load_demo","from sdmetrics.reports.single_table import QualityReport","from sdmetrics.single_column import CategoryCoverage"]}],"homepage":"https://docs.sdv.dev/sdmetrics","github":"https://github.com/sdv-dev/SDMetrics","docs":null,"changelog":"https://github.com/sdv-dev/SDMetrics/blob/main/HISTORY.md","pypi":"https://pypi.org/project/sdmetrics/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":16.3,"avg_import_s":4.78,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/sdmetrics/compatibility"},"provenance":{"verified_status":"passing","verified_at":"Sun Jun 28","last_verified":"Sun Jun 28","next_check":"Tue Jul 28","install_tag":null}}