| 书目名称 | Statistical Learning from a Regression Perspective |
| 编辑 | Richard A. Berk |
| 视频video | |
| 概述 | Accessible discussion of statistical learning procedures for practitioners.Lots of real applications discussed.Intuitive explanations and visual representation of underlying statistical concepts |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. . .Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. . .The material is w |
| 出版日期 | Book 20081st edition |
| 关键词 | Fitting; bagging; boosting; classification; random forests; statistical learning; support vector machines |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-77501-2 |
| isbn_ebook | 978-0-387-77501-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer Science+Business Media, LLC 2008 |
发表于 2025-3-21 18:59:16
