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183 |
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James, Gareth VerfasserIn (DE-588)1038457327 (DE-627)75743861X (DE-576)392417332 aut, An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Second edition, New York Springer 2021, ©2021, 1 Online-Ressource (616 pages), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Springer texts in statistics, Description based on publisher supplied metadata and other sources, Intro -- 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 Accuracy and 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 Summaries -- 2.4 Exercises -- 3 Linear Regression -- 3.1 Simple Linear Regression -- 3.1.1 Estimating the Coefficients -- 3.1.2 Assessing the Accuracy of the Coefficients Estimates -- 3.1.3 Assessing the Accuracy of the Model -- 3.2 Multiple Linear Regression -- 3.2.1 Estimating the Regression Coefficients -- 3.2.2 Some Important Questions -- 3.3 Other Considerations in the Regression Model -- 3.3.1 Qualitative Predictors -- 3.3.2 Extensions of the Linear Model -- 3.3.3 Potential Problems -- 3.4 The Marketing Plan -- 3.5 Comparison of Linear Regression with K-Nearest Neighbors -- 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 Terms -- 3.6.5 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 Multinomial Logistic Regression -- 4.4 Generative Models for Classification., Mathematical statistics DE-289, R (Computer program language) DE-289, Mathematical models DE-289, Statistics DE-289, Electronic books, Lehrbuch (DE-588)4123623-3 (DE-627)104270187 (DE-576)209561262 gnd-content, Einführung (DE-588)4151278-9 (DE-627)104450460 (DE-576)209786884 gnd-content, s (DE-588)4056995-0 (DE-627)106152955 (DE-576)209119799 Statistik gnd, s (DE-588)4705956-4 (DE-627)356147487 (DE-576)215406362 R Programm gnd, DE-101, s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd, (DE-627), s (DE-588)4129903-6 (DE-627)105706523 (DE-576)209614412 Regressionsanalyse gnd, s (DE-588)4288033-6 (DE-627)10428367X (DE-576)210820004 Resampling gnd, s (DE-588)4134827-8 (DE-627)104790636 (DE-576)209655577 Lineares Modell gnd, s (DE-588)4347788-4 (DE-627)156895994 (DE-576)211488801 Entscheidungsbaum gnd, s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd, s (DE-588)4070044-6 (DE-627)106101536 (DE-576)209179082 Cluster-Analyse gnd, Witten, Daniela VerfasserIn (DE-588)108120849X (DE-627)845688642 (DE-576)454037805 aut, Hastie, Trevor 1953- VerfasserIn (DE-588)172128242 (DE-627)697041832 (DE-576)167899988 aut, Tibshirani, Robert 1956- VerfasserIn (DE-588)172417740 (DE-627)697358836 (DE-576)167899996 aut, 9781071614174, Erscheint auch als Druck-Ausgabe James, Gareth An introduction to statistical learning Second edition New York, NY : Springer, 2021 xv, 607 Seiten (DE-627)1765978874 9781071614174 9781071614204, https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6686746 X:EBC Aggregator lizenzpflichtig |
| spellingShingle |
James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert, An introduction to statistical learning: with applications in R, Intro -- 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 Accuracy and 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 Summaries -- 2.4 Exercises -- 3 Linear Regression -- 3.1 Simple Linear Regression -- 3.1.1 Estimating the Coefficients -- 3.1.2 Assessing the Accuracy of the Coefficients Estimates -- 3.1.3 Assessing the Accuracy of the Model -- 3.2 Multiple Linear Regression -- 3.2.1 Estimating the Regression Coefficients -- 3.2.2 Some Important Questions -- 3.3 Other Considerations in the Regression Model -- 3.3.1 Qualitative Predictors -- 3.3.2 Extensions of the Linear Model -- 3.3.3 Potential Problems -- 3.4 The Marketing Plan -- 3.5 Comparison of Linear Regression with K-Nearest Neighbors -- 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 Terms -- 3.6.5 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 Multinomial Logistic Regression -- 4.4 Generative Models for Classification., Mathematical statistics, R (Computer program language), Mathematical models, Statistics, Electronic books, Lehrbuch, Einführung, Statistik, R Programm, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| title |
An introduction to statistical learning: with applications in R |
| title_auth |
An introduction to statistical learning with applications in R |
| title_full |
An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani |
| title_fullStr |
An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani |
| title_full_unstemmed |
An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani |
| title_short |
An introduction to statistical learning |
| title_sort |
an introduction to statistical learning with applications in r |
| title_sub |
with applications in R |
| title_unstemmed |
An introduction to statistical learning: with applications in R |
| topic |
Mathematical statistics, R (Computer program language), Mathematical models, Statistics, Electronic books, Lehrbuch, Einführung, Statistik, R Programm, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| topic_facet |
Mathematical statistics, R (Computer program language), Mathematical models, Statistics, Electronic books, Lehrbuch, Einführung, Statistik, R, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| url |
https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6686746 |
| work_keys_str_mv |
AT jamesgareth anintroductiontostatisticallearningwithapplicationsinr, AT wittendaniela anintroductiontostatisticallearningwithapplicationsinr, AT hastietrevor anintroductiontostatisticallearningwithapplicationsinr, AT tibshiranirobert anintroductiontostatisticallearningwithapplicationsinr, AT jamesgareth introductiontostatisticallearningwithapplicationsinr, AT wittendaniela introductiontostatisticallearningwithapplicationsinr, AT hastietrevor introductiontostatisticallearningwithapplicationsinr, AT tibshiranirobert introductiontostatisticallearningwithapplicationsinr |