| Description |
1 online resource |
| Contents |
1. Introduction and Overview℗ Lars Kotthoff, Raphael Sonabend, Natalie Foss, Bernd Bischl 2. Data and Basic ModelingNatalie Foss, Lars Kotthoff 3. Evaluation and BenchmarkingGiuseppe Casalicchio, Lukas Burk 4. Hyperparameter Optimization℗ Marc Becker, Lennart Schneider, Sebastian Fischer 5. Advanced Tuning Methods and Black Box OptimizationLennart Schneider, Marc Becker 6. Feature SelectionMarvin N. Wright 7. Sequential PipelinesMartin Binder, Florian Pfisterer 8. Non-sequential Pipelines and TuningMartin Binder, Florian Pfisterer, Marc Becker, Marvin N. Wright 9. PreprocessingJanek Thomas 10. Advanced Technical Aspects of mlr3℗ Michel Lang, Sebastian Fischer, Raphael Sonabend 11. Large-Scale Benchmarking Sebastian Fischer, Michel Lang, Marc Becker 12. Model Interpretation℗ Susanne Dandl, Przemys⁹́⁸CÌʹaw Biecek, Giuseppe Casalicchio, Marvin N. Wright 13. Beyond Regression and Classification℗ Raphael Sonabend, Patrick Schratz, Damir Pulatov 14. Algorithmic Fairness℗ Florian Pfisterer |
| Summary |
"Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components. Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the user for their application Convenient and expressive machine learning pipelining enabling advanced modelling Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Description based on print version record and CIP data provided by publisher; resource not viewed |
| Subject |
Machine learning -- Statistical methods
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R (Computer program language)
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Machine learning.
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MATHEMATICS / Probability & Statistics / General
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COMPUTERS / Mathematical & Statistical Software
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| Form |
Electronic book
|
| Author |
Bischl, Bernd, editor
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Sonabend, Raphael, editor
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Kotthoff, Lars, editor
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Lang, Michel (Data scientist), editor.
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| LC no. |
2023036495 |
| ISBN |
9781003402848 |
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1003402844 |
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9781003830573 |
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1003830579 |
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9781003830580 |
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1003830587 |
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