An introduction to statistical learning: with applications in Python
Gespeichert in:
| Beteiligte Personen: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer
[2023]
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| Schriftenreihe: | Springer texts in statistics
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| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://ebookcentral.proquest.com/lib/uniregensburg-ebooks/detail.action?docID=30614337 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 https://doi.org/10.1007/978-3-031-38747-0 |
| Abstract: | An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. |
| Umfang: | 1 Online-Ressource (XV, 607 Seiten) |
| ISBN: | 9783031387470 |
| ISSN: | 2197-4136 |
| DOI: | 10.1007/978-3-031-38747-0 |
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| id | DE-604.BV049032803 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-18T17:09:06Z |
| institution | BVB |
| isbn | 9783031387470 |
| issn | 2197-4136 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034295464 |
| oclc_num | 1389176221 |
| open_access_boolean | |
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| owner_facet | DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-898 DE-BY-UBR DE-861 DE-188 DE-523 DE-863 DE-BY-FWS DE-1050 DE-20 DE-862 DE-BY-FWS DE-92 DE-824 DE-703 DE-706 DE-739 DE-634 DE-M347 DE-573 DE-355 DE-BY-UBR DE-83 |
| physical | 1 Online-Ressource (XV, 607 Seiten) |
| psigel | ZDB-2-SMA ZDB-2-SEB ZDB-30-PQE ZDB-2-SMA_2023 gbd_0 ZDB-2-SMA FHM_Einzelkauf ZDB-30-PQE UBR Einzelkauf 2024 |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in statistics |
| spellingShingle | James, Gareth Witten, Daniela Hastie, Trevor 1953- Tibshirani, Robert 1956- Taylor, Jonathan E. An introduction to statistical learning with applications in Python Python (DE-588)118793772 gnd Statistical Theory and Methods Statistics and Computing Applied Statistics Statistics Mathematical statistics—Data processing Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)118793772 (DE-588)4123037-1 (DE-588)4056995-0 (DE-588)4193754-5 |
| title | An introduction to statistical learning with applications in Python |
| title_auth | An introduction to statistical learning with applications in Python |
| title_exact_search | An introduction to statistical learning with applications in Python |
| title_full | An introduction to statistical learning with applications in Python Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor |
| title_fullStr | An introduction to statistical learning with applications in Python Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor |
| title_full_unstemmed | An introduction to statistical learning with applications in Python Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor |
| title_short | An introduction to statistical learning |
| title_sort | an introduction to statistical learning with applications in python |
| title_sub | with applications in Python |
| topic | Python (DE-588)118793772 gnd Statistical Theory and Methods Statistics and Computing Applied Statistics Statistics Mathematical statistics—Data processing Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Python Statistical Theory and Methods Statistics and Computing Applied Statistics Statistics Mathematical statistics—Data processing Datenanalyse Statistik Maschinelles Lernen |
| url | https://doi.org/10.1007/978-3-031-38747-0 |
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