Data defines the model by dint of genetic programming, producing the best decile table.
GenIQ Articles: Analytic
Bruce Ratner, Ph.D.
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    1. Re-Data-Mining Your Constantly-updated Database: A Criterion for Doing So
    2. What Criteria Do You Use to Build a Model that Maximizes the Cum Lift?
    3. What Criteria Do You Use to Determine the Best Model?
    4. Top Five Statistical Modeling Problems: Nonissues for the Machine-learning GenIQ Model
    5. Statistical vs. Machine-Learning Data Mining
    6. CHAID-based Data Mining for Paired-Variable Assessment
    7. The Missing Statistic in the Decile Table: The Confidence Interval
    8. The Importance of Straight Data: Simplicity and Desirability for Good Model Building Practice
    9. The Paradox of Overfitting
    10. Building a Database Model to Outperform a Test Campaign
    11. To Fit or Not to Fit Data to a Model
    12. Assessing the Predictiveness of a Classification Model: Traditional vs. Modern Methods 
    13. Two-by-Two Classification and Decile Tables - A Comparison
    14. Genetic vs. Statistic Regression Models - A Comparison
    15. Your Customers are Talking: Are You Listening?
    16. Is Not a Response-Model Tree a Response-Model Tree by Any Other Name?
    17. Interpretation of Coefficient-free Models
    18. Social Network Analysis, Social Media Data, and Text Mining to Boost Business Intelligence
    19. Predictive Modeling Using Real-time Data
    20. Data Mining Quiz - II
    21. Data Mining Quiz
    22. CHAID: Nine Inventive, Utile Applications Beyond Its Original Intent
    23. Response-Approval Model: An Effective Approach for Implementation
    24. Data Mining: Illustration of the Pythagorean Theorem
    25. Stepwise is a Problematic Method for Variable Selection in Regression: Alternative Methods are Available
    26. What If There Were No Significance Testing?
    27. A Simple Method for Assessing Linear Trend and Seasonality Components in Database Models
    28. Variable Selection Methods in Regression: Ignorable Problem, Outing Notable Solution
    29. A New CRM Method for Identifying High-value Responders
    30. CRM Segmentation for Targeted Marketing
    31. Retain Best Customers and Maximize their Potential: A CRM Machine-learning Approach
    32. A New CRM Method for Identifying High-value Responders
    33. Predicting the Quality of Your Statistical Regression Models
    34. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    35. What is the GenIQ Model?
    36. Linear Probability, Logit, and Probit Models: How Do They Differ?
    37. A Database Marketing Regression Model that Maximizes Cum Lift
    38. A New Method of Modeling Missing Data: Deliverance of Discarded, Incomplete Cases
    39. Predicting Share of Wallet without Survey Data
    40. Variable Selection Methods in Regression: Many Statisticians Know Them, But Few Know They Produce Poorly Performing Models
    41. Statistical Modelers and Data Miners: Variable Selection, Data Mining Paradigm, Optimal Decile Table, and more ...
    42. The GenIQ Model: Data-defined, Data Mining, Variable Selection, and Decile Optimization
    43. When Data Are Too Large to Handle in the Memory of Your Computer
    44. How To Bootstrap
    45. Data Mining: An Ill-defined Concept
    46. HELP! I Need Somebody, Not Just Anybody ...
    47. Do-It-Yourself Method for Finding the Square Root of 2 
    48. GenIQ: A Visual Introduction
    49. Overfitting: Old Problem, New Solution
    50. Genetic Data Mining: The Correlation Coefficient
    51. Data Cleaning is Not Completed Until the “Noise” is Eliminated
    52. How to Make the Best Credit Score Even Better
    53. Multivariate Regression Trees: An Alternative Method
    54. "Grand" words (1000) about the GenIQ Model.
    55. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    56. Real World Data are Dirty: Data Cleaning and the "Noise" Problem
    57. GenIQ: For Modelers Who Daringly Consider a Different Model –
    58. The Most Compelling Illustration of the GenIQ Model
    59. A Most Compelling Illustration of the GenIQ Model
    60. GenIQ Lets the Data Specify the Model
    61. Data Mining Using Genetic Programming
    62. GenIQ-enhanced Regression Model
    63. GenIQ-enhanced/Data-reused Regression
    64. GenIQ: Nonlinear Curve Fitter
    65. GenIQ: OLS Curve Fitter
    66. A Method for Moderating Outliers, Instead of Discarding Them
    67. Building Statistical Regression Models: Straight Data are Necessary
    68. Logistic Regression versus Machine Learning Regression
    69. Ordinary Regression versus Machine Learning Regression
    70. The GenIQ Model: FAQs
    71. Interpreting Model Performance: Use the “Smart” Decile Analysis
    72. Predictor Variable Importance: Multicollinearity is Not a Problem for a Genetic Regression Model
    73. Dummy Variables: The Problem and Its Solution
    74. Finding the Best Variables for Database Marketing Models
    75. Decile Analysis Primer: Cum Lift for Response Model
    76. Maximizing the Lift in Database Marketing
    77. When Statistical Model Performance is Poor: Try Something New, and Try It Again
    78. A Hybrid Statistics-Machine Learning Paradigm for Database Response Modeling
    79. Tukey's Bulging Rule: Why Use It, and What to Do When It Fails
    80. Tukey's Bulging Rule for Straightening Data
    81. Modeling a Skewed Distribution with Many Zero Values
    82. A New Jackknife Method: 3-in-1 Tool for Variable Selection, Data Mining and Model Building
    83. A Genetic Model to Identify Titanic Survivors
    84. Statistics versus Machine Learning: A Significant Difference for Database Response Modeling
    85. The Genetic Programming Engine that Does: Data Specify the Model, Not Fit Data to a Model
    86. GenIQ-Parkinson's Law: The GenIQ Model Expands to Fill the Time Available for Model Completion
    87. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    88. A Genetic Imputation Method for Database Modeling
    89. Missing Value Analysis: A Machine-learning Approach
    90. A Genetic Logistic Regression Model: A Model-free Approach to Identifying Responders to a CRM Solicitation
    91. Predictive Analytics Now Accessible to Excel Spreadsheet Users: GenIQ Model Software with an Excel Toolbar
    92. An Alternative Response Model
    93. Analysis and Modeling for Today's Data
    94. Using the GenIQ Model to Insure the Validation of a Model is Unbiased
    95. Gain of a Predictive Information Advantage: Data Mining via Evolution
    96. Response-Approval Model: An Effective Approach for Implementation
    97. Marketing Optimization Model: A Genetic Approach
    98. Binary Logistic Regression: A Model-free Approach
    99. Ordinal Logistic Regression: A Model-free Approach
    100. Multinomial Logistic Regression: A Model-free Approach
    101. Quantile Regression: Model-free Approach
    102. Rethink The Regression Model: Think GenIQ Model
    103. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    104. A New Method of Decile Analysis Optimization for Database Models
    105. Multiple Catalog Mail Campaigns: Who Gets Mailed Next, and Which Catalog Should It Be?
    106. Building and Solving Response Optimization Models with the GenIQ Model
    107. Gaining Insights from Your Data: A Neoteric Machine Learning Method
    108. Data Mining for the Desktop
    109. Radically Distinctive Without Equal Predictive Model
    110. Extracting Nonlinear Dependencies: An Easy, Automatic Method
    111. Retail Revenue Optimization: Accounting for Profit-eating Markdowns

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at br@dmstat1.com.
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