Statistical learning from a regression perspective 978752321
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物品所在地：中國大陸
原出版社：世界圖書出版有限公司
物品所在地：中國大陸
原出版社：世界圖書出版有限公司
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| *完成訂單後正常情形下約兩周可抵台。 *本賣場提供之資訊僅供參考，以到貨標的為正確資訊。 印行年月：202403*若逾兩年請先於私訊洽詢存貨情況，謝謝。 台灣(台北市)在地出版社，每筆交易均開具統一發票，祝您中獎最高1000萬元。 書名：Statistical learning from a regression perspective ISBN：9787523211328 出版社：世界圖書出版有限公司 著編譯者：Richard A. Berk(著) 頁數：433 所在地：中國大陸 *此為代購商品 書號：1648542 可大量預訂，請先連絡。 【台灣高等教育出版社簡體書】 Statistical learning from a regression perspective 787523211328 Richard A. Berk(著) 內容簡介 本書把關注點集中在給定一組預測變數並且在數據分析開始之前缺乏可以指定的可靠模型時的響應變數的條件分佈上。與現代數據分析一致，它強調適當的統計學習數據分析以綜合方式依賴於健全的數據收集、智能數據管理、適當的統計程序和對結果的可理解的解釋。監督學習可被統一視為回歸分析的一種形式。通過大量實際應用及其相關的R代碼來說明關鍵概念和過程，著眼于實際意義。計算機科學和統計學的日益融合在這本教材中得到了很好的體現。目錄 1 Statistical Learning as a Regression Problem1 1 Getting Started 1 2 Setting the Regression Context 1 3 Revisiting the Ubiquitous Linear Regression Model 1 3 1 Problems in Practice 1 4 Working with Statistical Models that are Wrong 1 4 1 An Alternative Approach to Regression 1 4 2 More on Statistical Inference with Wrong Models 1 4 3 Introduction to Sandwich Standard Errors 1 4 4 Introduction to Conformal Inference 1 4 5 Introduction to the Nonparametric Bootstrap 1 4 6 Wrong Regression Models with Binary Response Variables 1 5 The Transition to Statistical Learning 1 5 1 Models Versus Algorithms 1 6 Some Initial Concepts 1 6 1 Overall Goals of Statistical Learning 1 6 2 Forecasting with Supervised Statistical Learning 1 6 3 Overfitting 1 6 4 Data Snooping 1 6 5 Some Constructive Responses to Overfitting and Data Snooping 1 6 6 Loss Functions and Related Concepts 1 6 7 The Bias-Variance Tradeoff 1 6 8 Linear Estimators 1 6 9 Degrees of Freedom 1 6 10 Basis Functions 1 6 11 The Curse of Dimensionality 1 7 Statistical Learning in Context Endnotes References 2 Splines， Smoothers， and Kernels 2 1 Introduction 2 2 Regression Splines 2 2 1 Piecewise Linear Population Approximations 2 2 2 Polynomial Regression Splines 2 2 3 Natural Cubic Splines 2 2 4 B-Splines 2 3 Penalized Smoothing 2 3 1 Shrinkage and Regularization 2 4 Penalized Regression Splines 2 4 1 An Application 2 5 Smoothing Splines 2 5 1 A Smoothing Splines Illustration 2 6 Locally Weighted Regression as a Smoother 2 6 1 Nearest Neighbor Methods 2 6 2 Locally Weighted Regression 2 7 Smoothers for Multiple Predictors 2 7 1 Smoothing in Two Dimensions 2 7 2 The Generalized Additive Model 2 8 Smoothers with Categorical Variables 2 8 1 An Illustration Using the Generalized Additive Model with a Binary Outcome 2 9 An Illustration of Statistical Inference After Model Selection 2 9 1 Level I Versus Level II Summary 2 10 Kernelized Regression 2 10 1 Radial Basis Kernel 2 10 2 ANOVA Radial Basis Kernel 2 10 3 A Kernel Regression Application 2 11 Summary and Conclusions Endnotes References 3 Classification and Regression Trees (CART) 3 1 Introduction 3 2 An Introduction to Recursive Partitioning in CART 3 3 The Basic Ideas in More Depth 3 3 1 Tree Diagrams for Showing What the Greedy Algorithm Determined 3 3 2 An Initial Application 3 3 3 Classification and Forecasting with CART 3 3 4 Confusion Tables 3 3 5 CART as an Adaptive Nearest Neighbor Method 3 4 The Formalities of Splitting a Node 3 5 An Illustrative Prison Inmate Risk Assessment Using CART 3 6 Classification Errors and Costs 3 6 1 Default Costs in CART 3 6 2 Prior Probabilities and Relative Misclassification Costs 3 7 Varying the Prior and the Complexity Parameter 3 8 An Example with Three Response Categories 3 9 Regression Trees 3 9 1 A CART Application for the Correlates of a Student's GPA in High School 3 10 Pruning 3 11 Missing Data 3 11 1 Missing Data with CART 3 12 More on CART Instability 3 13 Summary of Statistical Inference with CART 3 13 1 Summary of Statistical Inference for CART Forecasts 3 14 Overall Summary and Conclusions Exercises Endnotes References 4 Bagging 4 1 Introduction 4 2 The Bagging Algorithm 4 3 Some Bagging Details 4 3 1 Revisiting the CART Instability Problem 4 3 2 Resampling Methods for Bagging 4 3 3 Votes Over Trees and Probabilities 4 3 4 Forecasting and Imputation 4 3 5 Bagging Estimation and Statistical Inference 4 3 6 Margins for Classification 4 3 7 Using Out-of-Bag Observations as Test Data 4 3 8 Baggi 詳細資料或其他書籍請至台灣高等教育出版社查詢，查後請於PChome商店街私訊告知ISBN或書號，我們即儘速上架。 |
