Evaluation & Testing

PyCM

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

Status
Generally available
Last checked
Aging · Sep 2, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

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.

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.

When it fits — and when it doesn’t

A good fit 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.

Look elsewhere if

  • 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.

Known weaknesses

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

  1. 01

    Limited documentation and examples

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

  2. 02

    Small community and limited support

    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.

  3. 03

    Performance limitations with large datasets

    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.

  4. 04

    Limited support for advanced visualization features

    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.

What it does well

  • Supports multi-class confusion matrix evaluation

  • Handles both input data vectors and direct matrix inputs

  • Comprehensive support for various classes and overall statistics parameters

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.

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