The tutorial will be held by Bernd Bischl (home page, twitter) and Michel Lang (twitter) on August 7th, 15:00 UTC for approximately 3 hours. The event is hosted by the RLadies Galápagos.
Our tutorial introduces the package mlr3 for modern, state-of-the-art machine learning in R.
The mlr3 ecosystem provides a one-stop solution for all machine learning (ML) needs, spanning preprocessing, model learning and evaluation, ensembles, visualization, and hyperparameter tuning (via mlr3tuning).
Its pipeline system mlr3pipelines allows to easily express complex workflows, and to quickly prototype new ideas and applications.
Whether you are applying ML to solve a practical prediction problem, implementing learning algorithms as a research software engineer or researching ML by empirical means,
mlr3 can help make your workflow more readable and efficient.
The main objective of the tutorial is to introduce and familiarize users with mlr3 and its ecosystem.
This will allow participants to take advantage of its functionality for their own projects, in particular:
- how to benchmark and compare different machine learning approaches in a statistically sound manner,
- how to build complex machine learning workflows, including preprocessing and stacked ensembles,
- automatic hyperparameter tuning and pipeline optimization (AutoML),
- how to get the technical "nitty-gritties" for ML experiments right, e.g., speed up by parallelization, encapsulation of experiments in external processes or working on databases.
We target to users with at least basic knowledge of machine learning concepts who are not yet familiar with mlr3.
We require a working knowledge of R at medium level.
One should know R's general type system and basic operations.
Some experience with R6 and data.table definitely help, but is not essential.
Slides can be found in the pdf directory of this repository. The examples are rendered in the gallery with tag german credit.
Install these packages for the examples:
install.packages("mlr3")
install.packages("mlr3learners")
install.packages("data.table")
install.packages("mlr3viz")
install.packages("ggplot2")
install.packages("mlr3tuning")
install.packages("paradox")
install.packages("mlr3filters")
install.packages("remotes")
install.packages("praznik")
install.packages("ranger")
install.packages("kknn")
install.packages("xgboost")
install.packages("rchallenge")
install.packages("skimr")
install.packages("DataExplorer")
remotes::install_github("mlr-org/mlr3pipelines")
We also highly recommend to check out our book. The most time efficient way to get into the package is to read the cheat sheets.
The tutorial is split into 3 parts (55 min each, talk + use case), in the middle there will be a 15 min coffee break:
- mlr3
- mlr3tuning
- mlr3pipelines
At the end of each session we will answer questions asked and upvoted via sli.do.