{"product_id":"applied-machine-learning-using-mlr3-in-r-paperback","title":"Applied Machine Learning Using Mlr3 in R - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eBernd Bischl\u003c\/b\u003e (Editor), \u003cb\u003eRaphael Sonabend\u003c\/b\u003e (Editor), \u003cb\u003eLars Kotthoff\u003c\/b\u003e (Editor)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003emlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. \u003cb\u003eApplied Machine Learning Using mlr3 in R\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003eFeatures: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eIn-depth coverage of the mlr3 ecosystem for users and developers\u003c\/li\u003e \u003cli\u003eExplanation and illustration of basic and advanced machine learning concepts\u003c\/li\u003e \u003cli\u003eReady to use code samples that can be adapted by the user for their application\u003c\/li\u003e \u003cli\u003eConvenient and expressive machine learning pipelining enabling advanced modelling\u003c\/li\u003e \u003cli\u003eCoverage of topics that are often ignored in other machine learning books\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBernd Bischl\u003c\/b\u003e is a professor of Statistical Learning and Data Science in LMU Munich and co-director of the Munich Center for Machine Learning. He studied Computer Science, Artificial Intelligence and Data Science and holds a PhD in statistics. His research interests include AutoML, model selection, interpretable ML and the development of statistical software. He wrote the initial version of mlr and still leads the mlr3 developers, now largely focusing on design, code review and strategic development.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRaphael Sonabend\u003c\/b\u003e is a founder and director of OSPO Now and a visiting researcher at Imperial College London. They hold a PhD in statistics, specializing in machine learning applications for survival analysis. They wrote the mlr3 packages mlr3proba and mlr3benchmark.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eLars Kotthoff\u003c\/b\u003e is an associate professor of Computer Science at the University of Wyoming, US. He has studied and held academic appointments in Germany, UK, Ireland, and Canada. Lars has been contributing to mlr for about a decade. His research aims to automate machine learning and other areas of AI.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMichel Lang\u003c\/b\u003e is the scientific coordinator of the Research Center Trustworthy Data Science and Security. He has a PhD in statistics and has been developing statistical software for over a decade. He joined the mlr team in 2014 and wrote the initial version of mlr3.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 340\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.74 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 18, 2024\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43154421415999,"sku":"9781032507545","price":174.94,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/nRNCvxCUnA9781032507545.webp?v=1776956654","url":"https:\/\/dhlswag.com\/products\/applied-machine-learning-using-mlr3-in-r-paperback","provider":"BBB","version":"1.0","type":"link"}