A comprehensive Python library for generating and analyzing multi-class confusion matrices with extensive statistical metrics.
PyCM is a Python library for generating and analyzing multi-class confusion matrices, providing a comprehensive suite of statistical metrics for evaluating classification models. It solves the problem of needing a unified, robust tool to compute a wide range of performance indicators beyond basic accuracy, supporting both binary and multi-class scenarios. The library is designed as a post-classification evaluation toolkit, enabling data scientists to deeply understand model behavior and compare different classifiers effectively.
Data scientists, machine learning engineers, and researchers who need to evaluate and compare classification models, especially those working with multi-class or multilabel problems and requiring detailed statistical analysis.
Developers choose PyCM for its unparalleled breadth of metrics, ease of use, and specialized features like matrix comparison, parameter recommendation, and advanced visualization. It is a dedicated, open-source alternative to building custom evaluation scripts or relying on limited built-in functions from other libraries.
Multi-class confusion matrix library in Python
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Computes over 100 overall and class-based statistics, including niche parameters like Kappa and AUC, providing a comprehensive evaluation beyond standard metrics as highlighted in the README.
Handles confusion matrices for any number of classes and supports multilabel classification from version 4.0, with class-wise and sample-wise calculations, making it versatile for complex scenarios.
Includes plotting capabilities for confusion matrices, ROC, Precision-Recall, and other curves using Matplotlib or Seaborn, aiding in visual model interpretation with customizable options.
Allows benchmarking of multiple confusion matrices with overall and class-based scores via the Compare class, helping systematically identify the best-performing classifier.
Visualization features require Matplotlib (>=3.0.0) or Seaborn (>=0.9.1), adding extra dependencies and potential installation complexity, especially in constrained environments.
Calculating a vast array of metrics can be computationally intensive for large datasets, making it less suitable for high-speed or real-time applications compared to focused libraries.
The plethora of metrics and advanced features, such as parameter recommender and curve classes, might overwhelm users who only need simple evaluation, despite the comprehensive documentation.