Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
| Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner |
| Autore | Ratner Bruce |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2012 |
| Descrizione fisica | 1 online resource (524 p.) |
| Disciplina | 658.8/72 |
| Altri autori (Persone) | RatnerBruce |
| Soggetto topico |
Database marketing - Statistical methods
Data mining - Statistical methods |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-24862-8
1-4665-5121-6 1-280-12244-7 9786613526304 1-4398-6092-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression 16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison 25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models |
| Record Nr. | UNINA-9910457262503321 |
Ratner Bruce
|
||
| Boca Raton : , : Taylor & Francis, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
| Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner |
| Autore | Ratner Bruce |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2012 |
| Descrizione fisica | 1 online resource (524 p.) |
| Disciplina | 658.8/72 |
| Altri autori (Persone) | RatnerBruce |
| Soggetto topico |
Database marketing - Statistical methods
Data mining - Statistical methods |
| ISBN |
0-429-24862-8
1-4665-5121-6 1-280-12244-7 9786613526304 1-4398-6092-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression 16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison 25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models |
| Record Nr. | UNINA-9910778813003321 |
|
Ratner Bruce
|
||
| Boca Raton : , : Taylor & Francis, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
| Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner |
| Autore | Ratner Bruce |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2012 |
| Descrizione fisica | 1 online resource (524 p.) |
| Disciplina | 658.8/72 |
| Altri autori (Persone) | RatnerBruce |
| Soggetto topico |
Database marketing - Statistical methods
Data mining - Statistical methods |
| ISBN |
0-429-24862-8
1-4665-5121-6 1-280-12244-7 9786613526304 1-4398-6092-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression 16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison 25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models |
| Record Nr. | UNINA-9910821503903321 |
|
Ratner Bruce
|
||
| Boca Raton : , : Taylor & Francis, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||