The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman.
Material type:
TextSeries: Springer series in statisticsPublication details: New York, NY : Springer, 2017.Edition: 2nd edDescription: xxii, 745 p. : ill. (some col.) ; 25 cmISBN: - 9780387848570
- 006.3/1 22
- Q325.75 .H37 2009
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
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Zetech Library - TRC General Stacks | Non-fiction | Q325.75 .H37 2017 (Browse shelf(Opens below)) | C2 | Available | Z010870 | ||
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Zetech Library - TRC General Stacks | Non-fiction | Q325.75 .H37 2017 (Browse shelf(Opens below)) | C1 | Available | Z009609 |
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| Q325.5 .K55 2023 Fundamentals of predictive analytics with JMP / | Q325.5 .R38 2019 Hands on deep learning algorithms with python: Master deep learning algorithms with extensive math by implementing them using Tensorflow | Q325.75 .H37 2017 The elements of statistical learning : data mining, inference, and prediction / | Q325.75 .H37 2017 The elements of statistical learning : data mining, inference, and prediction / | Q334.I398 2006 Advances in applied artificial intelligence : | Q335 .R53 2009 Artificial intelligence / | Q335 .R87 2022 Artificial intelligence: A model approach/ |
Includes bibliographical references (p. [699]-727) and indexes.
Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basic expansions and regularization -- Kernel smoothing methods -- Mode;s assessment and selection -- Model assessment and selection -- Additive models,trees , and related methods --Boosting and additive trees -- Natural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest- neighbors Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphic models - High- dimensional problems : p>>n
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