Lars Kotthoff – författare

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 developersExplanation and illustration of basic and advanced machine learning conceptsReady to use code samples that can be adapted by the user for their applicationConvenient and expressive machine learning pipelining enabling advanced modellingCoverage of topics that are often ignored in other machine learning booksThe 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.

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 developersExplanation and illustration of basic and advanced machine learning conceptsReady to use code samples that can be adapted by the user for their applicationConvenient and expressive machine learning pipelining enabling advanced modellingCoverage of topics that are often ignored in other machine learning booksThe 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.

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.

It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.