| 书目名称 | Random Forests with R | | 编辑 | Robin Genuer,Jean-Michel Poggi | | 视频video | | | 概述 | Offers an application-oriented guide to CART trees and random forests.Covers a range of practical issues, and provides real-life examples and R codes.Particularly valuable for statisticians wishing to | | 丛书名称 | Use R! | | 图书封面 |  | | 描述 | .This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of comp | | 出版日期 | Book 2020 | | 关键词 | Random forests; Machine learning; Classification; Regression; Nearest neighbor; Variable selection; High d | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-56485-8 | | isbn_softcover | 978-3-030-56484-1 | | isbn_ebook | 978-3-030-56485-8Series ISSN 2197-5736 Series E-ISSN 2197-5744 | | issn_series | 2197-5736 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 16:36:44
