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 Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Table of Contents:
Introduction The Personal Computer and Statistics Statistics and Data Analysis EDA The EDA Paradigm EDA Weaknesses Small and Big Data Data Mining Paradigm Statistics and Machine Learning Statistical Data Mining References Two Basic Data Mining Methods for Variable Assessment Introduction Correlation Coefficient Scatterplots Data Mining Smoothed Scatterplot General Association Test Summary References CHAID-Based Data Mining for Paired-Variable Assessment Introduction The Scatterplot The Smooth Scatterplot Primer on CHAID CHAID-Based Data Mining for a Smoother Scatterplot Summary References Appendix The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice Introduction Straightness and Symmetry in Data Data Mining Is a High Concept The Correlation Coefficient Scatterplot of (xx3, yy3) Data Mining the Relationship of (xx3, yy3) What Is the GP-Based Data Mining Doing to the Data? Straightening a Handful of Variables and a Dozen of Two Baker’s Dozens of Variables Summary References Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data Introduction Scales of Measurement Stem-and-Leaf Display Box-and-Whiskers Plot Illustration of the Symmetrizing Ranked Data Method Summary References Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment Introduction EDA Reexpression Paradigm What Is the Big Deal? PCA Basics Exemplary Detailed Illustration Algebraic Properties of PCA Uncommon Illustration PCA in the Construction of a Quasi-Interaction Variable Summary The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They? Introduction Basics of the Correlation Coefficient Calculation of the Correlation Coefficient Rematching Calculation of the Adjusted Correlation Coefficient Implication of Rematching Summary Logistic Regression: The Workhorse of Response Modeling Introduction Logistic Regression Model Case Study Logits and Logit Plots The Importance of Straight Data Reexpressing for Straight Straight Data for Case Study Technique†s When Bulging Rule Does Not Apply Reexpressing MOS_OPEN Assessing the Importance of Variables Important Variables for Case Study Relative Importance of the Variables Best Subset of Variables for Case Study Visual Indicators of Goodness of Model Predictions Evaluating the Data Mining Work Smoothing a Categorical Variable Additional Data Mining Work for Case Study Summary Ordinary Regression: The Workhorse of Profit Modeling Introduction Ordinary Regression Model Mini Case Study Important Variables for Mini Case Study Best Subset of Variables for Case Study Suppressor Variable AGE Summary References Variable Selection Methods in Regression: Ignorable Problem, Notable Solution Introduction Background Frequently Used Variable Selection Methods Weakness in the Stepwise Enhanced Variable Selection Method Exploratory Data Analysis Summary References CHAID for Interpreting a Logistic Regression Model Introduction Logistic Regression Model Database Marketing Response Model Case Study CHAID Multivariable CHAID Trees CHAID Market Segmentation CHAID Tree Graphs Summary The Importance of the Regression Coefficient Introduction The Ordinary Regression Model Four Questions Important Predictor Variables P Values and Big Data Returning to Question 1 Effect of Predictor Variable on Prediction The Caveat Returning to Question 2 Ranking Predictor Variables by Effect on Prediction Returning to Question 3 Returning to Question 4 Summary References The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables Introduction Background Illustration of the Difference between Reliability and Validity Illustration of the Relationship between Reliability and Validity The Average Correlation Summary Reference CHAID for Specifying a Model with Interaction Variables Introduction Interaction Variables Strategy for Modeling with Interaction Variables Strategy Based on the Notion of a Special Point Example of a Response Model with an Interaction Variable CHAID for Uncovering Relationships Illustration of CHAID for Specifying a Model An Exploratory Look Database Implication Summary References Market Segmentation Classification Modeling with Logistic Regression Introduction Binary Logistic Regression Polychotomous Logistic Regression Model Model Building with PLR Market Segmentation Classification Model Summary CHAID as a Method for Filling in Missing Values Introduction Introduction to the Problem of Missing Data Missing Data Assumption CHAID Imputation Illustration CHAID Most Likely Category Imputation for a Categorical Variable Summary References Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling Introduction Some Definitions Illustration of a Flawed Targeting Effort Well-Defined Targeting Effort Predictive Profiles Continuous Trees Look-Alike Profiling Look-Alike Tree Characteristics Summary Assessment of Marketing Models Introduction Accuracy for Response Model Accuracy for Profit Model Decile Analysis and Cum Lift for Response Model Decile Analysis and Cum Lift for Profit Model Precision for Response Model Precision for Profit Model Separability for Response and Profit Models Guidelines for Using Cum Lift, HL/SWMAD, and CV Summary Bootstrapping in Marketing: A New Approach for Validating Models Introduction Traditional Model Validation Illustration Three Questions The Bootstrap How to Bootstrap Bootstrap Decile Analysis Validation Another Question Bootstrap Assessment of Model Implementation Performance Summary References Validating the Logistic Regression Model: Try Bootstrapping Introduction Logistic Regression Model The Bootstrap Validation Method Summary Reference Visualization of Marketing ModelsData Mining to Uncover Innards of a Model Introduction Brief History of the Graph Star Graph Basics Star Graphs for Single Variables Star Graphs for Many Variables Considered Jointly Profile Curves Method Illustration Summary References Appendix 1: SAS Code for Star Graphs for Each Demographic Variable about the Deciles Appendix 2: SAS Code for Star Graphs for Each Decile about the Demographic Variables Appendix 3: SAS Code for Profile Curves: All Deciles The Predictive Contribution Coefficient: A Measure of Predictive Importance Introduction Background Illustration of Decision Rule Predictive Contribution Coefficient Calculation of Predictive Contribution Coefficient Extra Illustration of Predictive Contribution Coefficient Summary Reference Regression Modeling Involves Art, Science, and Poetry, Too Introduction Shakespearean Modelogue Interpretation of the Shakespearean Modelogue Summary Reference Genetic and Statistic Regression Models: A Comparison Introduction Background Objective A Pithy Summary of the Development of Genetic Programming The GenIQ Model: A Brief Review of Its Objective and Salient Features The GenIQ Model: How It Works Summary References Data Reuse: A Powerful Data Mining Effect of the GenIQ Model Introduction Data Reuse? Illustration of Data Reuse Modified Data Reuse: A GenIQ-Enhanced Regression Model Summary A Data Mining Method for Moderating Outliers Instead of Discarding Them Introduction Background Moderating Outliers Instead of Discarding Them Summary Overfitting: Old Prˇoblem, New Solution Introduction Background The GenIQ Model Solution to Overfitting Summary The Importance of Straight Data: Revisited Introduction Restatement of Why It Is Important to Straighten Restatement of Section 4.6"Data Mining the Relationship of (xx3, yy3)" Summary The GenIQ Model: Its Definition and an Application Introduction What Is Optimization? What Is Genetic Modeling? Genetic Modeling: An Illustration Parameters for Controlling a Genetic Model Run Genetic Modeling: Strengths and Limitations Goals of Marketing Modeling The GenIQ Response Model The GenIQ Profit Case Study: Response Model Case Study: Profit Model Summary Reference Finding the Best Variables for Marketing Models Introduction Background Weakness in the Variable Selection Methods Goals of Modeling in Marketing Variable Selection with GenIQ Nonlinear Alternative to Logistic Regression Model Summary References Interpretation of Coefficient-Free Models Introduction The Linear Regression Coefficient The Quasi-Regression Coefficient for Simple Regression Models Partial Quasi-RC for the Everymodel Quasi-RC for a Coefficient-Free Model Summary