Applied Machine Learning Using mlr3 in R (eBook, PDF)

Redaktion: Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Sonabend, Raphael

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 356
• Erscheinungstermin: 18. Januar 2024
• Englisch
• ISBN-13: 9781003830573
• Artikelnr.: 69690414

Autorenporträt

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.

Inhaltsangabe

1. Introduction and Overview. 2. Data and Basic Modeling. 3. Evaluation and
Benchmarking. 4. Hyperparameter Optimization. 5. Advanced Tuning Methods
and Black Box Optimization. 6. Feature Selection. 7. Sequential Pipelines.
8. Non-sequential Pipelines and Tuning. 9. Preprocessing. 10. Advanced
Technical Aspects of mlr3 .11. Model Interpretation and Explanation. 12.
Model Interpretation. 13. Beyond Regression and Classification. 14.
Algorithmic Fairness.