Statistical Learning from a Regression Perspective (Springer Series in Statistics) pdf epub mobi txt 电子书下载 2025

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出版者:Springer

作者:Richard A. Berk

出品人:

页数:384

译者:

出版时间:2008-07-31

价格:USD 84.95

装帧:Hardcover

isbn号码:9780387775005

丛书系列:Springer Series in Statistics

图书标签:

统计学习
美國
统计学
統計學習
統計學
科普
數學
数据处理

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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 written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.

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书名使我想起一本很有名的书叫CSAPP: Computer Science - A Programmer's Perspective... 来说正题. 我大概草草翻了一下, 读得不太细, 所以只是一个大概的感觉, 而且人和人的口味都不同, 所以观点仅供参考. 这本看目录主要讲的就是CART然后RandomForest和GBM, 用的都是R的pac...

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