benhamner/Metrics

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1651 stars · 452 forks observed · 2026-08-28

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

A multi-language library providing implementations of supervised machine learning evaluation metrics such as AUC, log loss, MAE, RMSE, and average precision at K. It offers consistent metric implementations across Python, R, Haskell, and MATLAB/Octave.

Use cases

• compute auc and log loss for model evaluation
• calculate mean absolute error and rmse in python
• evaluate ranking models with map at k
• compare metric implementations across python and r
• score kaggle competition submissions with standard metrics
• compute quadratic weighted kappa for grading tasks

When to choose

• you need standard ML evaluation metrics in Python, R, Haskell, or MATLAB/Octave
• you want consistent metric definitions across multiple languages
• you need metrics like MAP@K or quadratic weighted kappa not in your framework

When to avoid

• you already use scikit-learn or a modern ML framework with built-in metrics
• you need actively maintained libraries with recent updates
• you need deep learning specific metrics

Facets

library · maturity maintenance

machine-learning benchmarking math machine-learning data-science python cross-platform evaluation-metrics r haskell matlab octave kaggle algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="benhamner/Metrics")