An introduction to statistical learning: with applications in R

Media type: E-Book

Title: An introduction to statistical learning : with applications in R

Contains: Preface; Contents; 1 Introduction; 2 Statistical Learning; 2.1 What Is Statistical Learning?; 2.1.1 Why Estimate f?; 2.1.2 How Do We Estimate f?; 2.1.3 The Trade-Off Between Prediction Accuracyand Model Interpretability; 2.1.4 Supervised Versus Unsupervised Learning; 2.1.5 Regression Versus Classification Problems; 2.2 Assessing Model Accuracy; 2.2.1 Measuring the Quality of Fit; 2.2.2 The Bias-Variance Trade-Off; 2.2.3 The Classification Setting; 2.3 Lab: Introduction to R; 2.3.1 Basic Commands; 2.3.2 Graphics; 2.3.3 Indexing Data; 2.3.4 Loading Data
2.3.5 Additional Graphical and Numerical Summaries2.4 Exercises; 3 Linear Regression; 3.1 Simple Linear Regression; 3.1.1 Estimating the Coefficients; 3.1.2 Assessing the Accuracy of the CoefficientEstimates; 3.1.3 Assessing the Accuracy of the Model; Residual Standard Error; R2 Statistic; 3.2 Multiple Linear Regression; 3.2.1 Estimating the Regression Coefficients; 3.2.2 Some Important Questions; One: Is There a Relationship Between the Response and Predictors?; Two: Deciding on Important Variables; Three: Model Fit; Four: Predictions; 3.3 Other Considerations in the Regression Model
3.3.1 Qualitative PredictorsPredictors with Only Two Levels; Qualitative Predictors with More than Two Levels; 3.3.2 Extensions of the Linear Model; Removing the Additive Assumption; Non-linear Relationships; 3.3.3 Potential Problems; 1. Non-linearity of the Data; 2. Correlation of Error Terms; 3. Non-constant Variance of Error Terms; 4. Outliers; 5. High Leverage Points; 6. Collinearity; 3.4 The Marketing Plan; 3.5 Comparison of Linear Regression with K-NearestNeighbors; 3.6 Lab: Linear Regression; 3.6.1 Libraries; 3.6.2 Simple Linear Regression; 3.6.3 Multiple Linear Regression
3.6.4 Interaction Terms3.6.5 Non-linear Transformations of the Predictors; 3.6.6 Qualitative Predictors; 3.6.7 Writing Functions; 3.7 Exercises; 4 Classification; 4.1 An Overview of Classification; 4.2 Why Not Linear Regression?; 4.3 Logistic Regression; 4.3.1 The Logistic Model; 4.3.2 Estimating the Regression Coefficients; 4.3.3 Making Predictions; 4.3.4 Multiple Logistic Regression; 4.3.5 Logistic Regression for >2 Response Classes; 4.4 Linear Discriminant Analysis; 4.4.1 Using Bayes' Theorem for Classification; 4.4.2 Linear Discriminant Analysis for p=1
4.4.3 Linear Discriminant Analysis for p>14.4.4 Quadratic Discriminant Analysis; 4.5 A Comparison of Classification Methods; 4.6 Lab: Logistic Regression, LDA, QDA, and KNN; 4.6.1 The Stock Market Data; 4.6.2 Logistic Regression; 4.6.3 Linear Discriminant Analysis; 4.6.4 Quadratic Discriminant Analysis; 4.6.5 K-Nearest Neighbors; 4.6.6 An Application to Caravan Insurance Data; 4.7 Exercises; 5 Resampling Methods; 5.1 Cross-Validation; 5.1.1 The Validation Set Approach; 5.1.2 Leave-One-Out Cross-Validation; 5.1.3 k-Fold Cross-Validation; 5.1.4 Bias-Variance Trade-Off for k-FoldCross-Validation
5.1.5 Cross-Validation on Classification Problems

Contributor: James, Gareth [Author]; Witten, Daniela [Author]; Hastie, Trevor [Author]; Tibshirani, Robert [Author]

Published: New York, NY: Springer, [2017]

Issue: corrected at 8th printing 2017

Extent: 1 Online-Ressource (XIV, 426 p. 150 illus., 146 illus. in color, digital)

Language: English

DOI: 10.1007/978-1-4614-7138-7

ISBN: 9781461471387

Identifier:

RVK notation: SK 840 : Spezielle statistische Verfahren

Keywords: Statistik > R
Statistik > Maschinelles Lernen > R
Regressionsanalyse > Resampling > Lineares Modell > Entscheidungsbaum > Support-Vektor-Maschine > Cluster-Analyse

Origination:

Footnote: Description based upon print version of record

Description: Introduction -- Statistical Learning -- Linear Regression -- Classification -- Resampling Methods -- Linear Model Selection and Regularization -- Moving Beyond Linearity -- Tree-Based Methods -- Support Vector Machines -- Unsupervised Learning -- Index.

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 to marketing to 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, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

An introduction to statistical learning - [corrected at 8th printing 2017]

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