xiv Preface
ing methods including error handling, multi-objective tuning, and tuning with Hyperband
and Bayesian optimization methods.
Part III: Pipelines and Preprocessing In Part III we introduce mlr3pipelines, which
allows users to implement complex ML workows easily. In Chapter 7 we will show you
how to build a pipeline out of discrete congurable operations and how to treat complex
pipelines as if they were any other machine learning model. In Chapter 8 we will build
on the previous chapter by introducing non-sequential pipelines, which can have multiple
branches that carry out operations concurrently. We will also demonstrate how to tune
pipelines, including how to tune which operations should be included in the pipeline. Finally,
in Chapter 9 we will put pipelines into practice by demonstrating how to solve common
problems that occur when tting ML models to messy data.
Part IV: Advanced Topics In the nal part of the book, we will look at advanced
methodology and technical details. This part of the book is more theory-heavy in some
sections to help ground the design and implementation decisions. We will begin by looking
at advanced technical details in Chapter 10 that are essential reading for advanced users
who require parallelization, custom error handling, or large databases. Chapter 11 will
build on all preceding chapters to introduce large-scale benchmarking experiments that
compare many models, tasks, and measures; including how to make use of mlr3 extension
packages for loading data, using high-performance computing clusters, and formal statistical
analysis of benchmark experiments. Chapter 12 will discuss dierent packages that are
compatible with mlr3 to provide model-agnostic interpretability for feature importance
and local explainability of individual predictions. Chapter 13 will then delve into detail on
domain-specic methods that are implemented in our extension packages including survival
analysis, density estimation, spatio-temporal analysis, and more. Readers may choose to
selectively read sections in this chapter depending on your use case (i.e., if you have domain-
specic problems to tackle), or to use these as introductions to new domains to explore.
Finally, Chapter 14 will introduce algorithmic fairness, which includes specialized measures
and methods to identify and reduce algorithmic biases.
Citing this book
This book is the culmination of many years worth of software design, coding, writing, and
editing. It is very important to us that all our contributors are credited appropriately.
Citation details of packages in the mlr3 ecosystem can be found in their respective GitHub
repositories.
When you are citing this book please cite chapters directly; citations can be found at the
end of each chapter. If you need to reference the full book please use:
Bischl, B., Sonabend, R., Kotthoff, L., & Lang, M. (Eds.). (2024).
"Applied Machine Learning Using mlr3 in R". CRC Press. https://mlr3book.mlr-org.com
@book{Bischl2024
title = {Applied Machine Learning Using {m}lr3 in {R}},
editor = {Bernd Bischl and Raphael Sonabend and Lars Kotthoff and Michel Lang},
url = {https://mlr3book.mlr-org.com},
year = {2024},
isbn = {9781032507545},
publisher = {CRC Press}
}