Applied Machine Learning Using mlr3 in R 1st Edition by Bernd Bischl 9781003830580 1003830587

Applied Machine Learning Using mlr3 in R 1st Edition Bernd Bischl – Ebook Instant Download/Delivery ISBN(s): 9781032515670, 1032515678

Product details:

• ISBN 10: 1032515678
• ISBN 13: 9781032515670
• Author: Bernd Bischl

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 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.

Table 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 Large-Scale Benchmarking

12 Model Interpretation

13 Beyond Regression and Classification

14 Algorithmic Fairness

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