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Applied statistical learning = with ...

Schonlau, Matthias.

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Applied statistical learning = with case studies in Stata /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied statistical learning/ by Matthias Schonlau.
Reminder of title:	with case studies in Stata /
Author:	Schonlau, Matthias.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xv, 332 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Statistical methods. -
Online resource:	https://doi.org/10.1007/978-3-031-33390-3
ISBN:	9783031333903

Applied statistical learning = with case studies in Stata /
Schonlau, Matthias.

Applied statistical learningwith case studies in Stata /[electronic resource] :by Matthias Schonlau. - Cham :Springer International Publishing :2023. - xv, 332 p. :ill., digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..

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.

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 book's 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.

ISBN: 9783031333903

Standard No.: 10.1007/978-3-031-33390-3doiSubjects--Uniform Titles:

Stata.
Subjects--Topical Terms:

921882
Machine learning
--Statistical methods.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Applied statistical learning = with case studies in Stata /
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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 book's 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.
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