Titlebook: Metalearning; Applications to Auto Pavel Brazdil,Jan N. van Rijn,Joaquin Vanschoren Book‘‘‘‘‘‘‘‘ 2022Latest edition The Editor(s) (if appli

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Pavel Brazdil,Jan N. van Rijn,Carlos Soares,Joaquin Vanschoren

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Pavel Brazdil,Jan N. van Rijn,Carlos Soares,Joaquin Vanschoren

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1611-2482 ate edition on the successful first edition https://link.spr.This open access book offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (AutoML), covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimi

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Book‘‘‘‘‘‘‘‘ 2022Latest edition covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data

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Metalearning Approaches for Algorithm Selection IImance models exploit information regarding the relative performance of base-level models, which can be either in the form of rankings or pairwise comparisons. This chapter discusses various methods that use this information in the search for the potentially best algorithm for the target task.

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Automating Workflow/Pipeline Designit is important to leverage prior experience. This topic is addressed in one of the sections, which discusses . that have proved to be useful in the past. The workflows/pipelines that have proved successful in the past can be retrieved and used as plans in future tasks. Thus, it is possible to exploit both planning and metalearning.