PyCM

Multi-class confusion matrix library for post-classification model evaluation.

EstablishedOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Cooling

License

Open Source

Data freshness

Aging · Jun 8, 2026

Overview

What is PyCM?

PyCM is a Python-based multi-class confusion matrix library that supports both input data vectors and direct matrix inputs, offering comprehensive support for various classes and overall statistics parameters to evaluate classification models effectively.

Key differentiator

PyCM stands out by offering detailed confusion matrix evaluations with support for both input vectors and direct matrices, making it an essential tool for thorough model assessment in machine learning projects.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Supports multi-class confusion matrix evaluationmedium

Handles both input data vectors and direct matrix inputsmedium

Comprehensive support for various classes and overall statistics parametersmedium

↓ Weaknesses

Limited documentation and exampleshigh

The official documentation lacks detailed explanations and practical use cases, making it difficult for new users to quickly understand how to implement PyCM effectively.

Small community and limited supportmedium

PyCM has a relatively small user base compared to other machine learning libraries, which can result in fewer resources, tutorials, and community-driven solutions for troubleshooting issues.

Performance limitations with large datasetshigh

PyCM may experience performance degradation when handling very large datasets due to its reliance on Python's native data structures and operations which are not optimized for high-performance computing tasks.

Limited support for advanced visualization featuresmedium

While PyCM provides basic functionalities, it lacks advanced visualization capabilities that are available in other libraries such as Matplotlib or Seaborn, limiting its usefulness for detailed analysis and reporting.

Fit analysis

Who is it for?

✓ Best for

Developers who need to evaluate the performance of multi-class classification models with detailed statistics.

Researchers working on machine learning projects requiring comprehensive model evaluation metrics.

✕ Not a fit for

Projects that require real-time analysis or streaming data processing, as PyCM is designed for post-processing and evaluation.

Applications where a graphical user interface (GUI) is preferred over command-line tools.

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 PyCM

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →