MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01701 a2200265 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20241025163303.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230317b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781071614204 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
510.5 |
| Item number |
JAM |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
James, Gareth |
| 9 (RLIN) |
9694 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Introduction to statistical learning |
| Remainder of title |
: with applications in R |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Name of publisher, distributor, etc. |
Springer |
| Date of publication, distribution, etc. |
2022 |
| Place of publication, distribution, etc. |
New York |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
607p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
Springer texts in Statistics |
| 9 (RLIN) |
10348 |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Table of Contents: 1. Introduction 2. Statistical Learning 3. Linear Regression 4. Classification 5. Resampling Methods 6. Linear Model Selection and Regularization 7. Moving Beyond Linearity 8. Tree-Based Methods 9. Support Vector Machines 10. Deep Learning 11. Survival Analysis and Censored Data 12. Unsupervised Learning 13. Multiple Testing |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This is a comprehensive textbook for the essential tools of modern statistical learning. It provides an accessible overview of the field and the most important modeling and prediction techniques. This book covers topics such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Statistical Decision |
| 9 (RLIN) |
10349 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine Learning |
| 9 (RLIN) |
5542 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data Analysis |
| 9 (RLIN) |
10350 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hastie, Trevor |
| 9 (RLIN) |
10351 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Witten, Daniela |
| 9 (RLIN) |
10352 |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tibshirani, Robert |
| 9 (RLIN) |
10353 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
