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Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data /
The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2011]
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| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.