# mlpack/mlpack

mlpack: a fast, header-only C++ machine learning library

Repository: https://github.com/mlpack/mlpack
Canonical: https://ross.abutalabs.com/products/mlpack
Homepage: https://www.mlpack.org/
Language: C++
License: NOASSERTION
License Family: other
Topics: machine-learning-library, c-plus-plus, deep-learning, nearest-neighbor-search, regression, machine-learning, hacktoberfest, scientific-computing
Last push: 2026-08-23T10:06:14+00:00

## Health v2 (maintenance only)
Score: 87/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 63, longevity 100
- inputs: {"age_days": 4277, "days_push": 10, "days_rel": 85, "gap_med": 98.0, "n_releases_24m": 7}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5703, forks 1720 (observed 2026-08-28T04:09:28.115696+00:00)

## What it is
mlpack is a fast, header-only C++ machine learning library built on Armadillo, ensmallen, and cereal, offering a wide range of algorithms for classification, regression, clustering, and nearest-neighbor search. It also ships command-line programs and bindings for Python, Julia, R, and Go.

## Use cases
- train classifiers and regressors in C++
- run fast nearest-neighbor and range searches
- use machine learning from Python, R, Julia, or Go via bindings
- deploy lightweight ML models in production C++ apps
- cluster data with k-means or mean shift
- detect objects with YOLOv3
- cross-compile ML to embedded systems like Raspberry Pi

## When to choose
- you need fast, lightweight C++ ML without heavy dependencies
- you want one library covering classification, regression, clustering, and tree-based spatial search
- you need ML bindings across multiple languages from a single core
- you target embedded or resource-constrained deployment

## When to avoid
- you primarily want deep learning with GPU acceleration and autodiff (PyTorch, TensorFlow)
- you need a Python-first ecosystem with rich notebooks and tooling
- you prefer writing C++ over higher-level ML frameworks
- you need the very latest research models not yet implemented in mlpack

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, image-processing, cli
- domain: machine-learning, data-science, developer-tools
- platform: cpp, python, cli, cross-platform, windows
- tags: header-only, bindings, nearest-neighbor-search, scientific-computing, armadillo, numfocus, algorithms, linux, macos

## Member repositories
- mlpack/mlpack (main) score 87

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:28.115696+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 2026-08-29T17:53:33.817488+00:00, confidence not recorded.
  - readme: https://github.com/mlpack/mlpack (fetched 2026-08-28T04:09:28.115696+00:00, sha dd6950ef42f2)
  - homepage: https://www.mlpack.org/ (fetched 2026-08-29T08:49:03.275767+00:00, sha b45eab11afa6)
  - site_page: https://www.mlpack.org/doc/index.html (fetched 2026-08-29T08:49:03.284662+00:00, sha ab0e25ae9437)
- Data as of 2026-08-30T08:39:29.467469+00:00.