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Applied Machine Learning Using mlr3 in R
Taschenbuch von Bernd Bischl (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

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.

Inhaltsverzeichnis

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.

Details
Erscheinungsjahr: 2024
Genre: Importe, Umwelt
Produktart: Nachschlagewerke
Rubrik: Ökologie
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781032507545
ISBN-10: 1032507543
Sprache: Englisch
Einband: Kartoniert / Broschiert
Redaktion: Bischl, Bernd
Kotthoff, Lars
Lang, Michel
Sonabend, Raphael
Hersteller: Taylor & Francis Ltd
Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Maße: 249 x 176 x 18 mm
Von/Mit: Bernd Bischl (u. a.)
Erscheinungsdatum: 18.01.2024
Gewicht: 0,718 kg
Artikel-ID: 127955097
Über den Autor

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.

Inhaltsverzeichnis

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.

Details
Erscheinungsjahr: 2024
Genre: Importe, Umwelt
Produktart: Nachschlagewerke
Rubrik: Ökologie
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781032507545
ISBN-10: 1032507543
Sprache: Englisch
Einband: Kartoniert / Broschiert
Redaktion: Bischl, Bernd
Kotthoff, Lars
Lang, Michel
Sonabend, Raphael
Hersteller: Taylor & Francis Ltd
Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Maße: 249 x 176 x 18 mm
Von/Mit: Bernd Bischl (u. a.)
Erscheinungsdatum: 18.01.2024
Gewicht: 0,718 kg
Artikel-ID: 127955097
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