Flexible and Robust Machine Learning Using mlr3 in R

Getting Started


Michel Lang, Raphael Sonabend, Lars Kotthoff, Bernd Bischl

Contributing authors

  • Marc Becker
  • Przemysław Biecek
  • Martin Binder
  • Bernd Bischl
  • Lukas Burk
  • Giuseppe Casalicchio
  • Sebastian Fischer
  • Natalie Foss
  • Lars Kotthoff
  • Michel Lang
  • Florian Pfisterer
  • Damir Pulatov
  • Lennart Schneider
  • Patrick Schratz
  • Raphael Sonabend
  • Marvin Wright

Welcome to the Machine Learning in R universe. This is the electronic version of the upcoming book Flexible and Robust Machine Learning Using mlr3 in R. This book will teach you about the mlr3 universe of packages, from some machine learning methodology to implementations of complex algorithmic pipelines. We will cover how to use the mlr3 family of packages for data processing, fitting and training of machine learning models, tuning and hyperparameter optimization, feature selection, pipelines, data preprocessing, and model interpretability. In addition we will look at how our interface works beyond classification and regression settings to other fields including survival analysis, clustering, and more. Finally we will demonstrate how you can contribute to our universe by creating packages, learners, measures, pipelines, and other features.

We hope you enjoy reading our book and always welcome comments and feedback. If you notice any mistakes in the book we would appreciate if you could open an issue in the mlr3book issue tracker. All content in this book is licenced under CC BY-NC 4.0.