• Stars
    star
    1,628
  • Rank 28,000 (Top 0.6 %)
  • Language
    R
  • License
    Other
  • Created almost 11 years ago
  • Updated 3 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Machine Learning in R

mlr

Package website: release | dev

Machine learning in R.

tic CRAN_Status_Badge cran checks CRAN Downloads StackOverflow lifecycle codecov

Deprecated

{mlr} is considered retired from the mlr-org team. We won't add new features anymore and will only fix severe bugs. We suggest to use the new mlr3 framework from now on and for future projects.

Not all features of {mlr} are already implemented in {mlr3}. If you are missing a crucial feature, please open an issue in the respective mlr3 extension package and do not hesitate to follow-up on it.

Installation

Release

install.packages("mlr")

Development

remotes::install_github("mlr-org/mlr")

Citing {mlr} in publications

Please cite our JMLR paper [bibtex].

Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all {mlr} related publications can be found here.

Introduction

R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious and error-prone wrappers to call the different algorithms and unify their respective output.

Additionally you need to implement infrastructure to

  • resample your models
  • optimize hyperparameters
  • select features
  • cope with pre- and post-processing of data and compare models in a statistically meaningful way.

As this becomes computationally expensive, you might want to parallelize your experiments as well. This often forces users to make crummy trade-offs in their experiments due to time constraints or lacking expert programming skills.

{mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. It is written in a way that you can extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms.

Furthermore, the package is nicely connected to the OpenML R package and its online platform, which aims at supporting collaborative machine learning online and allows to easily share datasets as well as machine learning tasks, algorithms and experiments in order to support reproducible research.

Features

  • Clear S3 interface to R classification, regression, clustering and survival analysis methods
  • Abstract description of learners and tasks by properties
  • Convenience methods and generic building blocks for your machine learning experiments
  • Resampling methods like bootstrapping, cross-validation and subsampling
  • Extensive visualizations (e.g. ROC curves, predictions and partial predictions)
  • Simplified benchmarking across data sets and learners
  • Easy hyperparameter tuning using different optimization strategies, including potent configurators like
    • iterated F-racing (irace)
    • sequential model-based optimization
  • Variable selection with filters and wrappers
  • Nested resampling of models with tuning and feature selection
  • Cost-sensitive learning, threshold tuning and imbalance correction
  • Wrapper mechanism to extend learner functionality in complex ways
  • Possibility to combine different processing steps to a complex data mining chain that can be jointly optimized
  • OpenML connector for the Open Machine Learning server
  • Built-in parallelization
  • Detailed tutorial

Miscellaneous

Simple usage questions are better suited at Stackoverflow using the mlr tag.

Please note that all of us work in academia and put a lot of work into this project - simply because we like it, not because we are paid for it.

New development efforts should go into {mlr3}. We have a own style guide which can easily applied by using the mlr_style from the styler package. See our wiki for more information.

Talks, Workshops, etc.

mlr-outreach holds all outreach activities related to {mlr} and {mlr3}.

More Repositories

1

mlr3

mlr3: Machine Learning in R - next generation
R
885
star
2

mlr3book

Online version of Bischl, B., Sonabend, R., Kotthoff, L., & Lang, M. (Eds.). (2024). "Applied Machine Learning Using mlr3 in R". CRC Press.
TeX
245
star
3

mlrMBO

Toolbox for Bayesian Optimization and Model-Based Optimization in R
R
187
star
4

mlr3pipelines

Dataflow Programming for Machine Learning in R
R
130
star
5

mlr3proba

Probabilistic Learning for mlr3
R
114
star
6

mlr3learners

Recommended learners for mlr3
R
87
star
7

mlr3extralearners

Extra learners for use in mlr3.
R
76
star
8

mlr-outreach

HTML
64
star
9

parallelMap

R package to interface some popular parallelization backends with a unified interface
R
57
star
10

mlr3tuning

Hyperparameter optimization package of the mlr3 ecosystem
R
51
star
11

mlr3spatiotempcv

Spatiotemporal resampling methods for mlr3
TeX
47
star
12

mlr3verse

Meta-package for installing/updating mlr3* packages.
R
45
star
13

mlr3spatial

Spatial objects within the mlr3 ecosystem
HTML
42
star
14

mlr3viz

Visualizations for mlr3
R
41
star
15

mlrCPO

Composable Preprocessing Operators for MLR
R
37
star
16

mlr3keras

Deep learning for mlr3
R
35
star
17

mcboost

Multi-Calibration & Multi-Accuracy Boosting for R
R
31
star
18

paradox

ParamHelpers Next Generation
R
27
star
19

ParamHelpers

Helpers for parameters in black-box optimization, tuning and machine learning.
R
25
star
20

mlr3mbo

Flexible Bayesian Optimization in R
R
23
star
21

mlr3torch

Deep learning framework for the mlr3 ecosystem based on torch
R
22
star
22

mlr3gallery

Case studies using mlr3
HTML
21
star
23

mlr3db

Data Backends to let mlr3 work transparently with (remote) data bases
R
21
star
24

mlr3cluster

Cluster analysis for mlr3
R
20
star
25

mlr3filters

Filter-based feature selection for mlr3
R
19
star
26

mlr3fselect

Feature selection package of the mlr3 ecosystem.
R
19
star
27

bbotk

Black-box optimization framework for R.
R
19
star
28

mlr3-learndrake

Template for using mlr3 with drake
HTML
18
star
29

mlr3hyperband

Successive Halving and Hyperband in the mlr3 ecosystem
R
18
star
30

mlr3temporal

Forecasting for mlr3
HTML
18
star
31

user2020

Material for the useR2020 tutorial
14
star
32

miesmuschel

Flexible Mixed Integer Evolutionary Strategies
R
14
star
33

mlr3fairness

mlr3 extension for Fairness in Machine Learning
HTML
13
star
34

mlr3benchmark

Analysis and tools for benchmarking in mlr3 and beyond.
R
12
star
35

mlr3tuningspaces

Collection of search spaces for hyperparameter optimization in the mlr3 ecosystem
R
12
star
36

farff

a faster arff parser
R
11
star
37

mlr3measures

Performance measures used in mlr3
R
11
star
38

mlr3cheatsheets

Cheat Sheets for mlr3 and Friends
HTML
11
star
39

mlr3misc

Miscellaneous helper functions for mlr3
R
10
star
40

mlr3website

The mlr3 quarto website and accomanying R package.
R
8
star
41

mlr-extralearner

R
8
star
42

mlr3survival

Survival analysis for mlr3
R
7
star
43

mlr3learners.template

Learner from package {<package>} for mlr3
R
5
star
44

mlr3batchmark

Connector between mlr3 and batchtools
R
5
star
45

mlr3docker

Docker Image for mlr3
Dockerfile
5
star
46

mlr3ordinal

Ordinal Regression for mlr3
R
5
star
47

mlr3multioutput

Multiple Targets for mlr3
R
4
star
48

mlr3-targets

R
4
star
49

rush

Parallel and distributed computing in R.
R
4
star
50

styler.mlr

{styler} mlr style guide
R
4
star
51

mlr3oml

Connect mlr3 with OpenML
R
4
star
52

mlr3automl

R
3
star
53

mlr3fda

Functional Data Analysis for mlr3
R
3
star
54

mlr-web

HTML
3
star
55

mlr3pkgdowntemplate

pkgdown template package for mlr* packages
SCSS
2
star
56

mlr3data

Data sets used in the book, gallery, or in examples of mlr3.
R
2
star
57

mlr-org-website

HTML
1
star
58

mlrcranlog

mlr-org cranlogs
R
1
star
59

mlr3summary

R
1
star