Statistical Learning from a Regression Perspective (Springer Series in Statistics) pdf epub mobi txt 電子書下載2025

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出版者:Springer

作者:Richard A. Berk

出品人:

頁數:384

译者:

出版時間:2008-07-31

價格:USD 84.95

裝幀:Hardcover

isbn號碼:9780387775005

叢書系列:Springer Series in Statistics

圖書標籤:

統計學習
美國
統計學
統計學習
統計學
科普
數學
數據處理

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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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書名使我想起一本很有名的書叫CSAPP: Computer Science - A Programmer's Perspective... 來說正題. 我大概草草翻瞭一下, 讀得不太細, 所以隻是一個大概的感覺, 而且人和人的口味都不同, 所以觀點僅供參考. 這本看目錄主要講的就是CART然後RandomForest和GBM, 用的都是R的pac...

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