# mlr

mlr3: Machine Learning in R - next generation

Repository: https://github.com/mlr-org/mlr3
Canonical: https://ross.abutalabs.com/products/mlr
Homepage: https://mlr3.mlr-org.com
Language: R
License: LGPL-3.0
License Family: copyleft
Topics: machine-learning, data-science, classification, regression, r, mlr3, r-package
Last push: 2026-08-31T08:24:38+00:00
Link (homepage): https://mlr3.mlr-org.com
Link (site_page): https://mlr3.mlr-org.com/news/index.html
Link (site_page): https://mlr-org.com/faq.html
Link (site_page): https://mlr3.mlr-org.com/authors.html

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 100, release rhythm 86, longevity 100
- inputs: {"age_days": 2929, "days_push": 2, "days_rel": 12, "gap_med": 37, "n_releases_24m": 16}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1080, forks 98 (observed 2026-09-01T02:14:05.037984+00:00)

## What it is
mlr/mlr3 is a machine learning framework for R providing a unified, object-oriented interface to many learning algorithms. It supports resampling, hyperparameter tuning, feature selection, benchmarking, and extension packages for survival analysis, clustering, and pipelines.

## Use cases
- train and compare machine learning models in R
- tune hyperparameters of an R model
- run benchmark experiments across multiple learners
- perform cross-validation resampling in R
- do feature selection for a predictive model
- build a machine learning pipeline in R
- fit survival or clustering models with a unified interface

## When to choose
- you work in R and want a unified interface to many ML algorithms
- you need rigorous resampling, tuning, and benchmarking infrastructure
- you want an extensible ecosystem (mlr3learners, mlr3pipelines, mlr3tuning)

## When to avoid
- you need the retired legacy mlr package for new projects - use mlr3 instead
- you primarily work in Python (consider scikit-learn)
- you need deep learning or GPU-heavy workflows

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, benchmarking, data-science
- domain: machine-learning, data-science
- platform: cross-platform
- tags: r-package, cran, hyperparameter-tuning, feature-selection, resampling, predictive-modeling, mlr3, ensemble-learning, statistics, r

## Member repositories
- mlr-org/mlr3 (main) score 95
- mlr-org/mlr (mirror) score 40

## Provenance
- Observed fields: from GitHub, fetched 2026-09-01T02:14:05.037984+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 2026-08-30T03:38:50.134838+00:00, confidence not recorded.
  - readme: https://github.com/mlr-org/mlr3 (fetched 2026-09-01T02:14:05.037984+00:00, sha 6c3206f19ee5)
  - homepage: https://mlr3.mlr-org.com (fetched 2026-08-29T11:13:41.094348+00:00, sha 99f0e7d91c6f)
  - site_page: https://mlr3.mlr-org.com/news/index.html (fetched 2026-08-29T11:13:41.103600+00:00, sha f245310bade3)
  - site_page: https://mlr-org.com/faq.html (fetched 2026-08-29T11:13:41.106316+00:00, sha 4505c087c88d)
  - site_page: https://mlr3.mlr-org.com/authors.html (fetched 2026-08-29T11:13:41.108203+00:00, sha e1c2fb428449)
- Data as of 2026-08-30T08:39:29.467469+00:00.