Metrics
Machine learning evaluation metrics library for Python.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Metrics?
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.
Key differentiator
“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.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The library is primarily designed for Python, which may pose challenges for developers working in other languages.
Metrics can exhibit slow performance when computing metrics on very large datasets due to the lack of optimized parallel processing capabilities.
TypeScript SDK is maintained by the community, which may lead to delays in updates and potential inconsistencies with the Python version.
Version migrations often require significant code changes, as seen from v0.1 to v0.2 where chain definitions had to be rewritten.
Fit analysis
Who is it for?
✓ Best 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.
✕ Not a fit for
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.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with Metrics
Step-by-step setup guide with code examples and common gotchas.