|
|
|
|
| LEADER |
00000cam a22000007i 4500 |
| 001 |
on1392163422 |
| 003 |
OCoLC |
| 005 |
20240808213014.0 |
| 006 |
m o d |
| 007 |
cr un|---aucuu |
| 008 |
230805s2023 sz ob 001 0 eng d |
| 040 |
|
|
|a YDX
|b eng
|c YDX
|d GW5XE
|d N$T
|d YDX
|d EBLCP
|d OCLCO
|d OCLCQ
|d UKAHL
|d OCLCQ
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCQ
|d OCLCF
|d OCLCQ
|d OCLCO
|d OCLCQ
|d SFB
|d OCLCO
|
| 019 |
|
|
|a 1396872533
|a 1397674616
|a 1401067821
|
| 020 |
|
|
|a 9783031333903
|q electronic book
|
| 020 |
|
|
|a 303133390X
|q electronic book
|
| 020 |
|
|
|z 3031333896
|
| 020 |
|
|
|z 9783031333897
|
| 024 |
7 |
|
|a 10.1007/978-3-031-33390-3
|2 doi
|
| 035 |
|
|
|a (OCoLC)1392163422
|z (OCoLC)1396872533
|z (OCoLC)1397674616
|z (OCoLC)1401067821
|
| 050 |
|
4 |
|a Q325.5
|b .A67 2023
|
| 049 |
|
|
|a HCDD
|
| 100 |
1 |
|
|a Schonlau, Matthias,
|d 1967-
|1 https://id.oclc.org/worldcat/entity/E39PBJrrvFdMcTxF6cmhFVpQMP
|
| 245 |
1 |
0 |
|a Applied statistical learning :
|b with case studies in Stata /
|c Matthias Schonlau.
|
| 264 |
|
1 |
|a Cham :
|b Springer,
|c 2023.
|
| 300 |
|
|
|a 1 online resource.
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Statistics and computing
|
| 505 |
0 |
|
|a Preface -- 1 Prologue -- 2 Statistical Learning: Concepts -- 3 Statistical Learning: Practical Aspects -- 4 Logistic Regression -- 5 Lasso and Friends -- 6 Working with Text Data -- 7 Nearest Neighbors -- 8 The Naive Bayes Classifier -- 9 Trees -- 10 Random Forests -- 11 Boosting -- 12 Support Vector Machines -- 13 Feature Engineering -- 14 Neural Networks -- 15 Stacking -- Index.
|
| 520 |
|
|
|a This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata. After introductory material on statistical learning concepts and practical aspects, each further chapter is devoted to a statistical learning algorithm or a group of related techniques. In particular, the book presents logistic regression, regularized linear models such as the Lasso, nearest neighbors, the Naive Bayes classifier, classification trees, random forests, boosting, support vector machines, feature engineering, neural networks, and stacking. It also explains how to construct n-gram variables from text data. Examples, conceptual exercises and exercises using software are featured throughout, together with case studies in Stata, mostly from the social sciences; true to the books goal to facilitate the use of modern methods of data science in the field. Although mainly intended for upper undergraduate and graduate students in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science.
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 588 |
0 |
|
|a Description based upon online resource; title from PDF title page (viewed August 14th, 2023).
|
| 630 |
0 |
0 |
|a Stata.
|
| 630 |
0 |
7 |
|a Stata.
|2 cantic
|
| 630 |
0 |
7 |
|a Stata
|2 fast
|
| 650 |
|
0 |
|a Machine learning
|x Statistical methods.
|
| 650 |
|
0 |
|a Neural networks (Computer science)
|
| 650 |
|
7 |
|a Machine learning
|x Statistical methods
|2 fast
|
| 650 |
|
7 |
|a Neural networks (Computer science)
|2 fast
|
| 650 |
|
7 |
|a Aprenentatge automàtic.
|2 thub
|
| 650 |
|
7 |
|a Estadística matemàtica.
|2 thub
|
| 650 |
|
7 |
|a Xarxes neuronals (Informàtica)
|2 thub
|
| 655 |
|
0 |
|a Electronic books.
|
| 655 |
|
7 |
|a Llibres electrònics.
|2 thub
|
| 776 |
0 |
8 |
|i Print version:
|z 3031333896
|z 9783031333897
|w (OCoLC)1376747172
|
| 830 |
|
0 |
|a Statistics and computing.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-33390-3
|y Click for online access
|
| 903 |
|
|
|a SPRING-ALL2023
|
| 994 |
|
|
|a 92
|b HCD
|