Ledolter, Johannes.
Data Mining and Business Analytics with R. - 1 online resource (365 pages) - New York Academy of Sciences Ser. . - New York Academy of Sciences Ser. .
Intro -- DATA MINING AND BUSINESS ANALYTICS WITH R -- CONTENTS -- Preface -- Acknowledgments -- 1. Introduction -- Reference -- 2. Processing the Information and Getting to Know Your Data -- 2.1 Example 1: 2006 Birth Data -- 2.2 Example 2: Alumni Donations -- 2.3 Example 3: Orange Juice -- References -- 3. Standard Linear Regression -- 3.1 Estimation in R -- 3.2 Example 1: Fuel Efficiency of Automobiles -- 3.3 Example 2: Toyota Used-Car Prices -- Appendix 3.A The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction -- References -- 4. Local Polynomial Regression: a Nonparametric Regression Approach -- 4.1 Model Selection -- 4.2 Application to Density Estimation and the Smoothing of Histograms -- 4.3 Extension to the Multiple Regression Model -- 4.4 Examples and Software -- References -- 5. Importance of Parsimony in Statistical Modeling -- 5.1 How Do We Guard Against False Discovery -- References -- 6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) -- 6.1 Example 1: Prostate Cancer -- 6.2 Example 2: Orange Juice -- References -- 7. Logistic Regression -- 7.1 Building a Linear Model for Binary Response Data -- 7.2 Interpretation of the Regression Coefficients in a Logistic Regression Model -- 7.3 Statistical Inference -- 7.4 Classification of New Cases -- 7.5 Estimation in R -- 7.6 Example 1: Death Penalty Data -- 7.7 Example 2: Delayed Airplanes -- 7.8 Example 3: Loan Acceptance -- 7.9 Example 4: German Credit Data -- References -- 8. Binary Classification, Probabilities, and Evaluating Classification Performance -- 8.1 Binary Classification -- 8.2 Using Probabilities to Make Decisions -- 8.3 Sensitivity and Specificity -- 8.4 Example: German Credit Data -- 9. Classification Using a Nearest Neighbor Analysis -- 9.1 The k-Nearest Neighbor Algorithm. 9.2 Example 1: Forensic Glass -- 9.3 Example 2: German Credit Data -- Reference -- 10. The Na�ive Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables -- 10.1 Example: Delayed Airplanes -- Reference -- 11. Multinomial Logistic Regression -- 11.1 Computer Software -- 11.2 Example 1: Forensic Glass -- 11.3 Example 2: Forensic Glass Revisited -- Appendix 11.A Specification of a Simple Triplet Matrix -- References -- 12. More on Classification and a Discussion on Discriminant Analysis -- 12.1 Fisher's Linear Discriminant Function -- 12.2 Example 1: German Credit Data -- 12.3 Example 2: Fisher Iris Data -- 12.4 Example 3: Forensic Glass Data -- 12.5 Example 4: MBA Admission Data -- Reference -- 13. Decision Trees -- 13.1 Example 1: Prostate Cancer -- 13.2 Example 2: Motorcycle Acceleration -- 13.3 Example 3: Fisher Iris Data Revisited -- 14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods -- 14.1 R Packages for Tree Construction -- 14.2 Chi-Square Automatic Interaction Detection (CHAID) -- 14.3 Ensemble Methods: Bagging, Boosting, and Random Forests -- 14.4 Support Vector Machines (SVM) -- 14.5 Neural Networks -- 14.6 The R Package Rattle: A Useful Graphical User Interface for Data Mining -- References -- 15. Clustering -- 15.1 k-Means Clustering -- 15.2 Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions -- 15.3 Hierarchical Clustering Procedures -- References -- 16. Market Basket Analysis: Association Rules and Lift -- 16.1 Example 1: Online Radio -- 16.2 Example 2: Predicting Income -- References -- 17. Dimension Reduction: Factor Models and Principal Components -- 17.1 Example 1: European Protein Consumption -- 17.2 Example 2: Monthly US Unemployment Rates. 18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares -- 18.1 Three Examples -- References -- 19. Text as Data: Text Mining and Sentiment Analysis -- 19.1 Inverse Multinomial Logistic Regression -- 19.2 Example 1: Restaurant Reviews -- 19.3 Example 2: Political Sentiment -- Appendix 19.A Relationship Between the Gentzkow Shapiro Estimate of "Slant" and Partial Least Squares -- References -- 20. Network Data -- 20.1 Example 1: Marriage and Power in Fifteenth Century Florence -- 20.2 Example 2: Connections in a Friendship Network -- References -- Appendix A: Exercises -- Exercise 1 -- Exercise 2 -- Exercise 3 -- Exercise 4 -- Exercise 5 -- Exercise 6 -- Exercise 7 -- Appendix B: References -- Index.
9781118593745
Electronic books.
Data Mining and Business Analytics with R. - 1 online resource (365 pages) - New York Academy of Sciences Ser. . - New York Academy of Sciences Ser. .
Intro -- DATA MINING AND BUSINESS ANALYTICS WITH R -- CONTENTS -- Preface -- Acknowledgments -- 1. Introduction -- Reference -- 2. Processing the Information and Getting to Know Your Data -- 2.1 Example 1: 2006 Birth Data -- 2.2 Example 2: Alumni Donations -- 2.3 Example 3: Orange Juice -- References -- 3. Standard Linear Regression -- 3.1 Estimation in R -- 3.2 Example 1: Fuel Efficiency of Automobiles -- 3.3 Example 2: Toyota Used-Car Prices -- Appendix 3.A The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction -- References -- 4. Local Polynomial Regression: a Nonparametric Regression Approach -- 4.1 Model Selection -- 4.2 Application to Density Estimation and the Smoothing of Histograms -- 4.3 Extension to the Multiple Regression Model -- 4.4 Examples and Software -- References -- 5. Importance of Parsimony in Statistical Modeling -- 5.1 How Do We Guard Against False Discovery -- References -- 6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) -- 6.1 Example 1: Prostate Cancer -- 6.2 Example 2: Orange Juice -- References -- 7. Logistic Regression -- 7.1 Building a Linear Model for Binary Response Data -- 7.2 Interpretation of the Regression Coefficients in a Logistic Regression Model -- 7.3 Statistical Inference -- 7.4 Classification of New Cases -- 7.5 Estimation in R -- 7.6 Example 1: Death Penalty Data -- 7.7 Example 2: Delayed Airplanes -- 7.8 Example 3: Loan Acceptance -- 7.9 Example 4: German Credit Data -- References -- 8. Binary Classification, Probabilities, and Evaluating Classification Performance -- 8.1 Binary Classification -- 8.2 Using Probabilities to Make Decisions -- 8.3 Sensitivity and Specificity -- 8.4 Example: German Credit Data -- 9. Classification Using a Nearest Neighbor Analysis -- 9.1 The k-Nearest Neighbor Algorithm. 9.2 Example 1: Forensic Glass -- 9.3 Example 2: German Credit Data -- Reference -- 10. The Na�ive Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables -- 10.1 Example: Delayed Airplanes -- Reference -- 11. Multinomial Logistic Regression -- 11.1 Computer Software -- 11.2 Example 1: Forensic Glass -- 11.3 Example 2: Forensic Glass Revisited -- Appendix 11.A Specification of a Simple Triplet Matrix -- References -- 12. More on Classification and a Discussion on Discriminant Analysis -- 12.1 Fisher's Linear Discriminant Function -- 12.2 Example 1: German Credit Data -- 12.3 Example 2: Fisher Iris Data -- 12.4 Example 3: Forensic Glass Data -- 12.5 Example 4: MBA Admission Data -- Reference -- 13. Decision Trees -- 13.1 Example 1: Prostate Cancer -- 13.2 Example 2: Motorcycle Acceleration -- 13.3 Example 3: Fisher Iris Data Revisited -- 14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods -- 14.1 R Packages for Tree Construction -- 14.2 Chi-Square Automatic Interaction Detection (CHAID) -- 14.3 Ensemble Methods: Bagging, Boosting, and Random Forests -- 14.4 Support Vector Machines (SVM) -- 14.5 Neural Networks -- 14.6 The R Package Rattle: A Useful Graphical User Interface for Data Mining -- References -- 15. Clustering -- 15.1 k-Means Clustering -- 15.2 Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions -- 15.3 Hierarchical Clustering Procedures -- References -- 16. Market Basket Analysis: Association Rules and Lift -- 16.1 Example 1: Online Radio -- 16.2 Example 2: Predicting Income -- References -- 17. Dimension Reduction: Factor Models and Principal Components -- 17.1 Example 1: European Protein Consumption -- 17.2 Example 2: Monthly US Unemployment Rates. 18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares -- 18.1 Three Examples -- References -- 19. Text as Data: Text Mining and Sentiment Analysis -- 19.1 Inverse Multinomial Logistic Regression -- 19.2 Example 1: Restaurant Reviews -- 19.3 Example 2: Political Sentiment -- Appendix 19.A Relationship Between the Gentzkow Shapiro Estimate of "Slant" and Partial Least Squares -- References -- 20. Network Data -- 20.1 Example 1: Marriage and Power in Fifteenth Century Florence -- 20.2 Example 2: Connections in a Friendship Network -- References -- Appendix A: Exercises -- Exercise 1 -- Exercise 2 -- Exercise 3 -- Exercise 4 -- Exercise 5 -- Exercise 6 -- Exercise 7 -- Appendix B: References -- Index.
9781118593745
Electronic books.