## The elements of statistical learning: data mining, inference, and prediction

(Book)

__Location__

__Call Number__

__Status__

__Last Check-In__**ASU Main (3rd floor)**

**APA Citation**(style guide)

Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, Springer.

**Chicago / Turabian - Author Date Citation**(style guide)

Hastie, Trevor, Robert, Tibshirani and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer.

**Chicago / Turabian - Humanities Citation**(style guide)

Hastie, Trevor, Robert, Tibshirani and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer, 2009.

**MLA Citation**(style guide)

Hastie, Trevor,, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, Springer, 2009.

**Note!**Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.

"During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics"--Jacket.

#### Notes

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Last Sierra Extract Time | Sep 18, 2022 08:18:20 AM |
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Last File Modification Time | Sep 18, 2022 08:18:41 AM |

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505 | 0 | 0 | |g 1.|t Introduction --|g 2.|t Overview of supervised learning --|g 3.|t Linear methods for regression --|g 4.|t Linear methods for classification --|g 5.|t Basis expansions and regularization --|g 6.|t Kernel smoothing methods --|g 7.|t Model assessment and selection --|g 8.|t Model inference and averaging --|g 9.|t Additive models, trees, and related methods --|g 10.|t Boosting and additive trees --|g 11.|t Neural networks --|g 12.|t Support vector machines and flexible discriminants --|g 13.|t Prototype methods and nearest-neighbors --|g 14.|t Unsupervised learning --|g 15.|t Random forests --|g 16.|t Ensemble learning --|g 17.|t Undirected graphical models --|g 18.|t High-dimensional problems: p>> N. |

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