# JuliaAI/MLJ.jl

A Julia machine learning framework

Repository: https://github.com/JuliaAI/MLJ.jl
Canonical: https://ross.abutalabs.com/products/mljjl
Homepage: https://juliaml.ai
Language: Julia
License: NOASSERTION
License Family: other
Topics: machine-learning, julia, pipelines, tuning, data-science, tuning-parameters, predictive-modeling, classification, regression, statistics, clustering, stacking, ensemble-learning, pipeline
Last push: 2026-08-20T08:35:45+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 82, longevity 100
- inputs: {"age_days": 2954, "days_push": 13, "days_rel": 42, "gap_med": 57, "n_releases_24m": 8}
- 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 1935, forks 159 (observed 2026-08-28T04:05:57.010893+00:00)

## What it is
MLJ.jl is a machine learning framework for Julia providing a common interface to over 200 models, with meta-algorithms for model selection, hyperparameter tuning, evaluation, and composition. It is an umbrella package unifying components distributed across the JuliaML ecosystem.

## Use cases
- train and tune machine learning models in Julia
- build composable ML pipelines
- compare and benchmark multiple models
- stack and ensemble models
- evaluate model performance with cross-validation
- hyperparameter optimization for predictive models
- classification and regression workflows

## When to choose
- you work in Julia and want a unified interface to many ML models
- you need flexible model composition, tuning, or stacking beyond simple pipelines
- you want reproducible, scalable predictive modeling workflows

## When to avoid
- you need a Python or R ecosystem with extensive third-party libraries
- you need deep learning with GPU training as the primary focus
- your team has no Julia experience and the project is short-lived

## Facets
- artifact type: framework
- maturity: stable
- function: machine-learning, workflow-automation, data-science
- domain: machine-learning, data-science, artificial-intelligence
- platform: jvm, cross-platform
- tags: julia, model-tuning, pipelines, ensembles, stacking, predictive-modeling

## Member repositories
- JuliaAI/MLJ.jl (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:57.010893+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 2026-08-30T03:08:26.873975+00:00, confidence not recorded.
  - readme: https://github.com/JuliaAI/MLJ.jl (fetched 2026-08-28T04:05:57.010893+00:00, sha bdb5dbb1c626)
  - homepage: https://juliaml.ai (fetched 2026-08-29T10:47:46.676062+00:00, sha 733df7118304)
  - site_page: https://juliaml.ai/about (fetched 2026-08-29T10:47:46.680546+00:00, sha f2246e46bf08)
- Data as of 2026-08-30T08:39:29.467469+00:00.