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Applied Machine Learning Using mlr3 in R

Applied Machine Learning Using mlr3 in R

Applied Machine Learning Using mlr3 in R
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Applied Machine Learning Using mlr3 in R Paperback - 2024

by Bernd Bischl

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Details

  • Title Applied Machine Learning Using mlr3 in R
  • Author Bernd Bischl
  • Binding Paperback
  • Condition New
  • Pages 340
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2024-01-18
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # SKU143631
  • ISBN 9781032507545 / 1032507543
  • Weight 1.36 lbs (0.62 kg)
  • Dimensions 10 x 7 x 0.74 in (25.40 x 17.78 x 1.88 cm)
  • Category Mathematics
  • Library of Congress subjects R (Computer program language), Machine learning - Statistical methods
  • Library of Congress Catalogue Number 2023036494
  • Dewey Decimal Code 006.31
  • Quantity available 1

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From the publisher

mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. This book gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R.

About the author

Bernd Bischl is a professor of Statistical Learning and Data Science in LMU Munich and co-director of the Munich Center for Machine Learning. He studied Computer Science, Artificial Intelligence and Data Science and holds a PhD in statistics. His research interests include AutoML, model selection, interpretable ML and the development of statistical software. He wrote the initial version of mlr and still leads the mlr3 developers, now largely focusing on design, code review and strategic development.

Raphael Sonabend is a founder and director of OSPO Now and a visiting researcher at Imperial College London. They hold a PhD in statistics, specializing in machine learning applications for survival analysis. They wrote the mlr3 packages mlr3proba and mlr3benchmark.

Lars Kotthoff is an associate professor of Computer Science at the University of Wyoming, US. He has studied and held academic appointments in Germany, UK, Ireland, and Canada. Lars has been contributing to mlr for about a decade. His research aims to automate machine learning and other areas of AI.

Michel Lang is the scientific coordinator of the Research Center Trustworthy Data Science and Security. He has a PhD in statistics and has been developing statistical software for over a decade. He joined the mlr team in 2014 and wrote the initial version of mlr3.

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