Aktion	Zweigstelle	Standorte	Status	Frist	Vorbestellungen
Vorbestellen	Zweigstelle: 07., Urban-Loritz-Pl. 2a	Standorte: NN.MNS FS.E An In / College 6a - Englisch	Status: Verfügbar	Frist:	Vorbestellungen: 0

"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)

 

 

 

Aus dem Inhalt:

Preface vii // 1 Introduction 1 // 2 Statistical Learning 15 / 2.1 What Is Statistical Learning? 15 / 2.1.1 Why Estimate f? 17 / 2.1.2 How Do We Estimate f? 21 / 2.1.3 The Trade-Off Between Prediction Accuracy and Model Interpretability 24 / 2.1.4 Supervised Versus Unsupervised Learning 26 / 2.1.5 Regression Versus Classification Problems 28 / 2.2 Assessing Model Accuracy 29 / 2.2.1 Measuring the Quality of Fit 29 / 2.2.2 The Bias-Variance Trade-Off 33 / 2.2.3 The Classification Setting 37 / 2.3 Lab: Introduction to R 42 / 2.3.1 Basic Commands 42 / 2.3.2 Graphics 45 / 2.3.3 Indexing Data 47 / 2.3.4 Loading Data 48 / 2.3.5 Additional Graphical and Numerical Summaries 49 / 2.4 Exercises 52 // 3 Linear Regression 59 / 3.1 Simple Linear Regression 61 / 3.1.1 Estimating the Coefficients 61 / 3.1.2 Assessing the Accuracy of the Coefficient Estimates 63 / 3.1.3 Assessing the Accuracy of the Model 68 / 3.2 Multiple Linear Regression 71 / 3.2.1 Estimating the Regression Coefficients 72 / 3.2.2 Some Important Questions 75 / 3.3 Other Considerations in the Regression Model 82 / 3.3.1 Qualitative Predictors 82 / 3.3.2 Extensions of the Linear Model 86 / 3.3.3 Potential Problems 92 / 3.4 The Marketing Plan 102 / 3.5 Comparison of Linear Regression with K-Nearest Neighbors 104 / 3.6 Lab: Linear Regression 109 / 3.6.1 Libraries 109 / 3.6.2 Simple Linear Regression 110 / 3.6.3 Multiple Linear Regression 113 / 3.6.4 Interaction Terms 115 / 3.6.5 Non-linear Transformations of the Predictors 115 / 3.6.6 Qualitative Predictors 117 / 3.6.7 Writing Functions 119 / 3.7 Exercises 120 // 4 Classification 127 / 4.1 An Overview of Classification 128 / 4.2 Why Not Linear Regression? 129 / 4.3 Logistic Regression 130 / 4.3.1 The Logistic Model 131 / 4.3.2 Estimating the Regression Coefficients 133 / 4.3.3 Making Predictions 134 / 4.3.4 Multiple Logistic Regression 135 / 4.3.5 Logistic Regression for >2 Response Classes 137 / 4.4 Linear Discriminant Analysis 138 / 4.4.1 Using Bayes' Theorem for Classification 138 / 4.4.2 Linear Discriminant Analysis for p = 1 139 / 4.4.3 Linear Discriminant Analysis for p >1 142 / 4.4.4 Quadratic Discriminant Analysis 149 / 4.5 A Comparison of Classification Methods 151 / 4.6 Lab: Logistic Regression, LDA, QDA, and KNN 154 / 4.6.1 The Stock Market Data 154 / 4.6.2 Logistic Regression 156 / 4.6.3 Linear Discriminant Analysis 161 / 4.6.4 Quadratic Discriminant Analysis 163 / 4.6.5 K-Nearest Neighbors 163 / 4.6.6 An Application to Caravan Insurance Data 165 / 4.7 Exercises 168 // 5 Resampling Methods 175 / 5.1 Cross-Validation 176 / 5.1.1 The Validation Set Approach 176 / 5.1.2 Leave-One-Out Cross-Validation 178 / 5.1.3 k-Fold Cross-Validation 181 / 5.1.4 Bias-Variance Trade-Off for k-Fold Cross-Validation 183 / 5.1.5 Cross-Validation on Classification Problems 184 / 5.2 The Bootstrap 187 / 5.3 Lab: Cross-Validation and the Bootstrap 190 / 5.3.1 The Validation Set Approach 191 / 5.3.2 Leave-One-Out Cross-Validation 192 / 5.3.3 k-Fold Cross-Validation 193 / 5.3.4 The Bootstrap 194 / 5.4 Exercises 197 // 6 Linear Model Selection and Regularization 203 / 6.1 Subset Selection 205 / 6.1.1 Best Subset Selection 205 / 6.1.2 Stepwise Selection 207 / 6.1.3 Choosing the Optimal Model 210 / 6.2 Shrinkage Methods 214 / 6.2.1 Ridge Regression 215 / 6.2.2 The Lasso 219 / 6.2.3 Selecting the Tuning Parameter 227 / 6.3 Dimension Reduction Methods 228 / 6.3.1 Principal Components Regression 230 / 6.3.2 Partial Least Squares 237 / 6.4 Considerations in High Dimensions 238 / 6.4.1 High-Dimensional Data 238 / 6.4.2 What Goes Wrong in High Dimensions? 239 / 6.4.3 Regression in High Dimensions 241 / 6.4.4 Interpreting Results in High Dimensions 243 / 6.5 Lab 1: Subset Selection Methods 244 / 6.5.1 Best Subset Selection 244 / 6.5.2 Forward and Backward Stepwise Selection 247 / 6.5.3 Choosing Among Models Using the Validation Set Approach and Cross-Validation 248 / 6.6 Lab 2: Ridge Regression and the Lasso 251 / 6.6.1 Ridge Regression 251 / 6.6.2 The Lasso 255 / 6.7 Lab 3: PCR and PLS Regression 256 / 6.7.1 Principal Components Regression 256 / 6.7.2 Partial Least Squares 258 / 6.8 Exercises 259 // 7 Moving Beyond Linearity 265 / 7.1 Polynomial Regression 266 / 7.2 Step Functions 268 / 7.3 Basis Functions 270 / 7.4 Regression Splines 271 / 7.4.1 Piecewise Polynomials 271 / 7.4.2 Constraints and Splines 271 / 7.4.3 The Spline Basis Representation 273 / 7.4.4 Choosing the Number and Locations of the Knots 274 / 7.4.5 Comparison to Polynomial Regression 276 / 7.5 Smoothing Splines 277 / 7.5.1 An Overview of Smoothing Splines 277 / 7.5.2 Choosing the Smoothing Parameter ? 278 / 7.6 Local Regression 280 / 7.7 Generalized Additive Models 282 / 7.7.1 GAMs for Regression Problems 283 / 7.7.2 GAMs for Classification Problems 286 / 7.8 Lab: Non-linear Modeling 287 / 7.8.1 Polynomial Regression and Step Functions 288 / 7.8.2 Splines 293 / 7.8.3 GAMs 294 / 7.9 Exercises 297 // 8 Tree-Based Methods 303 / 8.1 The Basics of Decision Trees 303 / 8.1.1 Regression Trees 304 / 8.1.2 Classification Trees 311 / 8.1.3 Trees Versus Linear Models 314 / 8.1.4 Advantages and Disadvantages of Trees 315 / 8.2 Bagging, Random Forests, Boosting 316 / 8.2.1 Bagging 316 / 8.2.2 Random Forests 319 / 8.2.3 Boosting 321 / 8.3 Lab: Decision Trees 323 / 8.3.1 Fitting Classification Trees 323 / 8.3.2 Fitting Regression Trees 327 / 8.3.3 Bagging and Random Forests 328 / 8.3.4 Boosting 330 / 8.4 Exercises 332 // 9 Support Vector Machines 337 / 9.1 Maximal Margin Classifier 338 / 9.1.1 What Is a Hyperplane? 338 / 9.1.2 Classification Using a Separating Hyperplane 339 / 9.1.3 The Maximal Margin Classifier 341 / 9.1.4 Construction of the Maximal Margin Classifier 342 / 9.1.5 The Non-separable Case 343 / 9.2 Support Vector Classifiers 344 / 9.2.1 Overview of the Support Vector Classifier 344 / 9.2.2 Details of the Support Vector Classifier 345 / 9.3 Support Vector Machines 349 / 9.3.1 Classification with Non-linear Decision Boundaries 349 / 9.3.2 The Support Vector Machine 350 / 9.3.3 An Application to the Heart Disease Data 354 / 9.4 SVMs with More than Two Classes 355 / 9.4.1 One-Versus-One Classification 355 / 9.4.2 One-Versus-All Classification 356 / 9.5 Relationship to Logistic Regression 356 / 9.6 Lab: Support Vector Machines 359 / 9.6.1 Support Vector Classifier 359 / 9.6.2 Support Vector Machine 363 / 9.6.3 ROC Curves 365 / 9.6.4 SVM with Multiple Classes 366 / 9.6.5 Application to Gene Expression Data 366 / 9.7 Exercises 368 // 10 Unsupervised Learning 373 / 10.1 The Challenge of Unsupervised Learning 373 / 10.2 Principal Components Analysis 374 / 10.2.1 What Are Principal Components? 375 / 10.2.2 Another Interpretation of Principal Components 379 / 10.2.3 More on PCA 380 / 10.2.4 Other Uses for Principal Components 385 / 10.3 Clustering Methods 385 / 10.3.1 K-Means Clustering 386 / 10.3.2 Hierarchical Clustering 390 / 10.3.3 Practical Issues in Clustering 399 / 10.4 Lab 1: Principal Components Analysis 401 / 10.5 Lab 2: Clustering 404 / 10.5.1 K-Means Clustering 404 / 10.5.2 Hierarchical Clustering 406 / 10.6 Lab 3: NCI60 Data Example 407 / 10.6.1 PCA on the NCI60 Data 408 / 10.6.2 Clustering the Observations of the NCI60 Data 410 / 10.7 Exercises 413 // Index 419