mlr3

Package website: release | dev

Efficient, object-oriented programming on the building blocks of machine learning. Successor of mlr.

Resources (for users and developers)

• We have written a book. This should be the central entry point to the package.
• The mlr-org website includes for example a gallery with case studies.
• Reference manual
• FAQ
• Ask questions on Stackoverflow (tag mlr3)
• Extension Learners
  • Recommended core regression, classification, and survival learners are in mlr3learners
  • All others are in mlr3extralearners
  • Use the learner search to get a simple overview
• Cheatsheets
  • Overview of cheatsheets
  • mlr3
  • mlr3tuning
  • mlr3pipelines
• Videos:
  • useR2019 talk on mlr3
  • useR2019 talk on mlr3pipelines and mlr3tuning
  • useR2020 tutorial on mlr3, mlr3tuning and mlr3pipelines
• Courses/Lectures
  • The course Introduction to Machine learning (I2ML) is a free and open flipped classroom course on the basics of machine learning. mlr3 is used in the demos and exercises.
• Templates/Tutorials
  • mlr3-targets: Tutorial showcasing how to use {mlr3} with targets for reproducible ML workflow automation.
  • Using mlr3viz and animint2 for interactive visualizations.
• List of extension packages
• mlr-outreach contains public talks and slides resources.
• Wiki: Contains mainly information for developers.

Installation

Install the last release from CRAN:

install.packages("mlr3")

Install the development version from GitHub:

# install.packages("pak")
pak::pak("mlr-org/mlr3")

If you want to get started with mlr3, we recommend installing the mlr3verse meta-package which installs mlr3 and some of the most important extension packages:

install.packages("mlr3verse")

Example

Constructing Learners and Tasks

library(mlr3)

# create learning task
task_penguins = as_task_classif(species ~ ., data = palmerpenguins::penguins)
task_penguins

## 
## ── <TaskClassif> (344x8) ─────────────────────────────────────────────
## • Target: species
## • Properties: multiclass
## • Features (7):
##   • int (3): body_mass_g, flipper_length_mm, year
##   • dbl (2): bill_depth_mm, bill_length_mm
##   • fct (2): island, sex
## • Target classes: Adelie (44%), Gentoo (36%), Chinstrap (20%)

# load learner and set hyperparameter
learner = lrn("classif.rpart", cp = .01)

Basic train + predict

# train/test split
split = partition(task_penguins, ratio = 0.67)

# train the model
learner$train(task_penguins, split$train_set)

# predict data
prediction = learner$predict(task_penguins, split$test_set)

# calculate performance
prediction$confusion

##            truth
## response    Adelie Chinstrap Gentoo
##   Adelie       146         5      0
##   Chinstrap      6        63      1
##   Gentoo         0         0    123

measure = msr("classif.acc")
prediction$score(measure)

## classif.acc 
##   0.9651163

Resample

# 3-fold cross validation
resampling = rsmp("cv", folds = 3L)

# run experiments
rr = resample(task_penguins, learner, resampling)

# access results
rr$score(measure)[, .(task_id, learner_id, iteration, classif.acc)]

##                     task_id    learner_id iteration classif.acc
## 1: palmerpenguins::penguins classif.rpart         1   0.8956522
## 2: palmerpenguins::penguins classif.rpart         2   0.9478261
## 3: palmerpenguins::penguins classif.rpart         3   0.9649123

rr$aggregate(measure)

## classif.acc 
##   0.9361302

Extension Packages

Consult the wiki for short descriptions and links to the respective repositories.

For beginners, we strongly recommend to install and load the mlr3verse package for a better user experience.

This is the beginning of the README. Read the rest at the original. Read the full README ↗