sepandhaghighi/pycm

Health v2 · maintenance only

Adoption not part of the score

1506 stars · 126 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyCM is a Python library for computing multi-class confusion matrices and a wide range of per-class and overall evaluation statistics. It accepts both actual/predicted vectors and direct matrices, making it a comprehensive post-classification model evaluation tool for data scientists.

Use cases

• evaluate a multi-class classifier with confusion matrix metrics
• compute accuracy, precision, recall, and F1 scores in Python
• compare performance of several classification models
• generate a confusion matrix from predicted and actual label vectors
• get per-class and overall statistics for a predictive model
• analyze classification results for a research paper

When to choose

• you need a broad array of confusion matrix statistics beyond what scikit-learn exposes
• you work with multi-class classification and want per-class and overall metrics in one tool
• you want to build a confusion matrix directly from a matrix or from label vectors

When to avoid

• you only need simple binary accuracy and already use scikit-learn
• you need model training or hyperparameter tuning rather than evaluation
• you work outside the Python ecosystem

Facets

library · maturity stable

machine-learning data-science analytics math machine-learning data-science artificial-intelligence analytics python cross-platform confusion-matrix classification-metrics model-evaluation statistics

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markdown · JSON · MCP: product_card(name="sepandhaghighi/pycm")