mlr

Health v2 · maintenance only

Adoption not part of the score

1080 stars · 98 forks observed · 2026-09-01

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

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

framework · maturity active

machine-learning benchmarking data-science machine-learning data-science cross-platform r-package cran hyperparameter-tuning feature-selection resampling predictive-modeling mlr3 ensemble-learning statistics r

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mlr-org/mlr3")