# benhamner/Metrics

Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Repository: https://github.com/benhamner/Metrics
Canonical: https://ross.abutalabs.com/products/benhamner-metrics
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-01-11T06:01:24+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 5195, "days_push": 1330, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1651, forks 452 (observed 2026-08-28T04:05:16.931799+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: machine-learning, benchmarking, math
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: evaluation-metrics, r, haskell, matlab, octave, kaggle, algorithms

## Member repositories
- benhamner/Metrics (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.931799+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:01.430035+00:00, confidence not recorded.
  - readme: https://github.com/benhamner/Metrics (fetched 2026-08-28T04:05:16.931799+00:00, sha e6a1ed46600f)
- Data as of 2026-08-30T08:39:29.467469+00:00.