Observability & Monitoring

Metrics

Machine learning evaluation metrics library for Python.

Status
UnmaintainedNo commits in 4004 days.
Last checked
Checked · Oct 12, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

Metrics is an open-source Python library that provides a wide range of machine learning evaluation metrics. It helps developers and data scientists assess the performance of their models accurately, ensuring reliable outcomes in various applications.

Metrics stands out by offering a broad spectrum of evaluation metrics in a single, easy-to-use Python library, making it an essential tool for both research and production environments.

When it fits — and when it doesn’t

A good fit for

  • Data scientists who need a comprehensive set of evaluation metrics for their machine learning projects.

  • Developers working on Python-based machine learning applications requiring robust performance assessment tools.

Look elsewhere if

  • Projects that require real-time metric computation as Metrics is designed for batch processing.

  • Teams looking for a web-based UI to visualize and compare model performance.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited support for non-Python ecosystems

    The library is primarily designed for Python, which may pose challenges for developers working in other languages.

  2. 02

    Performance issues with large datasets

    Metrics can exhibit slow performance when computing metrics on very large datasets due to the lack of optimized parallel processing capabilities.

  3. 03

    Dependence on community for non-Python support

    TypeScript SDK is maintained by the community, which may lead to delays in updates and potential inconsistencies with the Python version.

  4. 04

    Frequent breaking changes between versions

    Version migrations often require significant code changes, as seen from v0.1 to v0.2 where chain definitions had to be rewritten.

What it does well

  • Wide range of evaluation metrics for classification and regression tasks.

  • Easy to integrate into existing Python projects.

  • Comprehensive documentation and examples.

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

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Set up Metrics

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →