├── LICENSE ├── README.md ├── pdfs ├── .gitignore ├── mlr3_slides.pdf ├── mlr3pipelines_slides.pdf └── mlr3tuning_slides.pdf └── rmds └── .gitignore /LICENSE: -------------------------------------------------------------------------------- 1 | Creative Commons Legal Code 2 | 3 | CC0 1.0 Universal 4 | 5 | CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE 6 | LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN 7 | ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS 8 | INFORMATION ON AN "AS-IS" BASIS. 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Affirmer understands and acknowledges that Creative Commons is not a 120 | party to this document and has no duty or obligation with respect to 121 | this CC0 or use of the Work. 122 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # useR! 2020 online tutorial on mlr3 2 | 3 | The tutorial will be held by Bernd Bischl ([home page](https://www.slds.stat.uni-muenchen.de/), [twitter](https://twitter.com/BBischl)) and Michel Lang ([twitter](https://twitter.com/michellangts)) on [August 7th, 15:00 UTC](https://arewemeetingyet.com/UTC/2020-08-07/15:00/useR!%202020%20Tutorial:%20mlr3) for approximately 3 hours. 4 | The event is hosted by the [RLadies Galápagos](https://twitter.com/rladiesgps). 5 | 6 | **UPDATE**: The recording can be found on [Youtube](https://youtu.be/T43hO2o_nZw). 7 | 8 | ## About 9 | 10 | Our tutorial introduces the package [mlr3](https://mlr3.mlr-org.com) for modern, state-of-the-art machine learning in R. 11 | 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](https://mlr3tuning.mlr-org.com)). 12 | Its pipeline system [mlr3pipelines](https://mlr3pipelines.mlr-org.com) allows to easily express complex workflows, and to quickly prototype new ideas and applications. 13 | 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, 14 | `mlr3` can help make your workflow more readable and efficient. 15 | 16 | 17 | ## Learning Objectives 18 | 19 | The main objective of the tutorial is to introduce and familiarize users with `mlr3` and its ecosystem. 20 | This will allow participants to take advantage of its functionality for their own projects, in particular: 21 | 22 | * how to benchmark and compare different machine learning approaches in a statistically sound manner, 23 | * how to build complex machine learning workflows, including preprocessing and stacked ensembles, 24 | * automatic hyperparameter tuning and pipeline optimization (AutoML), 25 | * 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. 26 | 27 | 28 | ## Target Audience 29 | 30 | We target to users with at least basic knowledge of machine learning concepts who are not yet familiar with `mlr3`. 31 | We require a working knowledge of R at medium level. 32 | One should know R's general type system and basic operations. 33 | Some experience with [R6](https://cran.r-project.org/package=R6) and [data.table](https://cran.r-project.org/package=data.table) definitely help, but is not essential. 34 | 35 | 36 | ## Course Materials 37 | 38 | Slides can be found in the pdf directory of this repository. 39 | The examples are rendered in the [gallery](https://mlr3gallery.mlr-org.com/) with tag [german credit](https://mlr3gallery.mlr-org.com/#german_credit). 40 | 41 | Install these packages for the examples: 42 | 43 | ```{r} 44 | install.packages("mlr3") 45 | install.packages("mlr3learners") 46 | install.packages("data.table") 47 | install.packages("mlr3viz") 48 | install.packages("ggplot2") 49 | install.packages("mlr3tuning") 50 | install.packages("paradox") 51 | install.packages("mlr3filters") 52 | install.packages("remotes") 53 | install.packages("praznik") 54 | install.packages("ranger") 55 | install.packages("kknn") 56 | install.packages("xgboost") 57 | install.packages("rchallenge") 58 | install.packages("skimr") 59 | install.packages("DataExplorer") 60 | remotes::install_github("mlr-org/mlr3pipelines") 61 | ``` 62 | 63 | We also highly recommend to check out our [book](https://mlr3book.mlr-org.com/). 64 | The most time efficient way to get into the package is to read the [cheat sheets](https://cheatsheets.mlr-org.com/). 65 | 66 | ## Schedule 67 | 68 | The tutorial is split into 3 parts (55 min each, talk + use case), in the middle there will be a 15 min coffee break: 69 | 70 | 1. mlr3 71 | 2. mlr3tuning 72 | 3. mlr3pipelines 73 | 74 | At the end of each session we will answer questions asked and upvoted via [sli.do](https://app.sli.do/event/tazsdetb). 75 | -------------------------------------------------------------------------------- /pdfs/.gitignore: -------------------------------------------------------------------------------- 1 | !.gitignore 2 | -------------------------------------------------------------------------------- /pdfs/mlr3_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mlr-org/user2020/cc18c1fd392cbc5a69b74d26d70fab1ceb6156ab/pdfs/mlr3_slides.pdf -------------------------------------------------------------------------------- /pdfs/mlr3pipelines_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mlr-org/user2020/cc18c1fd392cbc5a69b74d26d70fab1ceb6156ab/pdfs/mlr3pipelines_slides.pdf -------------------------------------------------------------------------------- /pdfs/mlr3tuning_slides.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mlr-org/user2020/cc18c1fd392cbc5a69b74d26d70fab1ceb6156ab/pdfs/mlr3tuning_slides.pdf -------------------------------------------------------------------------------- /rmds/.gitignore: -------------------------------------------------------------------------------- 1 | !.gitignore 2 | --------------------------------------------------------------------------------