Description

Book Synopsis

mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. 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 use

    Table of Contents

    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.

Applied Machine Learning Using mlr3 in R

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    A Paperback by Bernd Bischl, Raphael Sonabend, Lars Kotthoff

    £61.99

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    Order before 4pm today for delivery by Sat 24 Oct 2026.

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      Book details

      Publisher Chapman & Hall/CRC
      Published 1/18/2024 12:01:00 AM
      ISBN-13 9781032507545
      978-1032507545
      ISBN-10 1032507543
      Also in:
      Mathematics Probability and statistics Technology, Engineering & Agriculture Automatic control engineering Environmental science, engineering and technology

      Description

      Book Synopsis

      mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. 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 use

        Table of Contents

        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.

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