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. Validating the Logistic Regression Model: Try Bootstrapping
    2. Regression Modeling Involves Art, Science, and Poetry Too
    3. Re-Data-Mining Your Constantly-updated Database: A Criterion for Doing So
    4. What Criteria Do You Use to Build a Model that Maximizes the Cum Lift?
    5. What Criteria Do You Use to Determine the Best Model?
    6. Top Five Statistical Modeling Problems: Nonissues for the Machine-learning GenIQ Model
    7. Statistical vs. Machine-Learning Data Mining
    8. CHAID-based Data Mining for Paired-Variable Assessment
    9. The Missing Statistic in the Decile Table: The Confidence Interval
    10. The Importance of Straight Data: Simplicity and Desirability for Good Model Building Practice
    11. The Paradox of Overfitting
    12. Building a Database Model to Outperform a Test Campaign
    13. To Fit or Not to Fit Data to a Model
    14. Assessing the Predictiveness of a Classification Model: Traditional vs. Modern Methods 
    15. Two-by-Two Classification and Decile Tables - A Comparison
    16. Genetic vs. Statistic Regression Models - A Comparison
    17. Your Customers are Talking: Are You Listening?
    18. Is Not a Response-Model Tree a Response-Model Tree by Any Other Name?
    19. Interpretation of Coefficient-free Models
    20. Social Network Analysis, Social Media Data, and Text Mining to Boost Business Intelligence
    21. Predictive Modeling Using Real-time Data
    22. Data Mining Quiz - II
    23. Data Mining Quiz
    24. CHAID: Nine Inventive, Utile Applications Beyond Its Original Intent
    25. Response-Approval Model: An Effective Approach for Implementation
    26. Data Mining: Illustration of the Pythagorean Theorem
    27. Stepwise is a Problematic Method for Variable Selection in Regression: Alternative Methods are Available
    28. What If There Were No Significance Testing?
    29. A Simple Method for Assessing Linear Trend and Seasonality Components in Database Models
    30. Variable Selection Methods in Regression: Ignorable Problem, Outing Notable Solution
    31. A New CRM Method for Identifying High-value Responders
    32. CRM Segmentation for Targeted Marketing
    33. Retain Best Customers and Maximize their Potential: A CRM Machine-learning Approach
    34. A New CRM Method for Identifying High-value Responders
    35. Predicting the Quality of Your Statistical Regression Models
    36. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    37. What is the GenIQ Model?
    38. Linear Probability, Logit, and Probit Models: How Do They Differ?
    39. A Database Marketing Regression Model that Maximizes Cum Lift
    40. A New Method of Modeling Missing Data: Deliverance of Discarded, Incomplete Cases
    41. Predicting Share of Wallet without Survey Data
    42. Variable Selection Methods in Regression: Many Statisticians Know Them, But Few Know They Produce Poorly Performing Models
    43. Statistical Modelers and Data Miners: Variable Selection, Data Mining Paradigm, Optimal Decile Table, and more ...
    44. The GenIQ Model: Data-defined, Data Mining, Variable Selection, and Decile Optimization
    45. When Data Are Too Large to Handle in the Memory of Your Computer
    46. How To Bootstrap
    47. Data Mining: An Ill-defined Concept
    48. HELP! I Need Somebody, Not Just Anybody ...
    49. Do-It-Yourself Method for Finding the Square Root of 2 
    50. GenIQ: A Visual Introduction
    51. Overfitting: Old Problem, New Solution
    52. Genetic Data Mining: The Correlation Coefficient
    53. Data Cleaning is Not Completed Until the “Noise” is Eliminated
    54. How to Make the Best Credit Score Even Better
    55. Multivariate Regression Trees: An Alternative Method
    56. "Grand" words (1000) about the GenIQ Model.
    57. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    58. Real World Data are Dirty: Data Cleaning and the "Noise" Problem
    59. GenIQ: For Modelers Who Daringly Consider a Different Model –
    60. The Most Compelling Illustration of the GenIQ Model
    61. A Most Compelling Illustration of the GenIQ Model
    62. GenIQ Lets the Data Specify the Model
    63. Data Mining Using Genetic Programming
    64. GenIQ-enhanced Regression Model
    65. GenIQ-enhanced/Data-reused Regression
    66. GenIQ: Nonlinear Curve Fitter
    67. GenIQ: OLS Curve Fitter
    68. A Method for Moderating Outliers, Instead of Discarding Them
    69. Building Statistical Regression Models: Straight Data are Necessary
    70. Logistic Regression versus Machine Learning Regression
    71. Ordinary Regression versus Machine Learning Regression
    72. The GenIQ Model: FAQs
    73. Interpreting Model Performance: Use the “Smart” Decile Analysis
    74. Predictor Variable Importance: Multicollinearity is Not a Problem for a Genetic Regression Model
    75. Dummy Variables: The Problem and Its Solution
    76. Finding the Best Variables for Database Marketing Models
    77. Decile Analysis Primer: Cum Lift for Response Model
    78. Maximizing the Lift in Database Marketing
    79. When Statistical Model Performance is Poor: Try Something New, and Try It Again
    80. A Hybrid Statistics-Machine Learning Paradigm for Database Response Modeling
    81. Tukey's Bulging Rule: Why Use It, and What to Do When It Fails
    82. Tukey's Bulging Rule for Straightening Data
    83. Modeling a Skewed Distribution with Many Zero Values
    84. A New Jackknife Method: 3-in-1 Tool for Variable Selection, Data Mining and Model Building
    85. A Genetic Model to Identify Titanic Survivors
    86. Statistics versus Machine Learning: A Significant Difference for Database Response Modeling
    87. The Genetic Programming Engine that Does: Data Specify the Model, Not Fit Data to a Model
    88. GenIQ-Parkinson's Law: The GenIQ Model Expands to Fill the Time Available for Model Completion
    89. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    90. A Genetic Imputation Method for Database Modeling
    91. Missing Value Analysis: A Machine-learning Approach
    92. A Genetic Logistic Regression Model: A Model-free Approach to Identifying Responders to a CRM Solicitation
    93. Predictive Analytics Now Accessible to Excel Spreadsheet Users: GenIQ Model Software with an Excel Toolbar
    94. An Alternative Response Model
    95. Analysis and Modeling for Today's Data
    96. Using the GenIQ Model to Insure the Validation of a Model is Unbiased
    97. Gain of a Predictive Information Advantage: Data Mining via Evolution
    98. Response-Approval Model: An Effective Approach for Implementation
    99. Marketing Optimization Model: A Genetic Approach
    100. Binary Logistic Regression: A Model-free Approach
    101. Ordinal Logistic Regression: A Model-free Approach
    102. Multinomial Logistic Regression: A Model-free Approach
    103. Quantile Regression: Model-free Approach
    104. Rethink The Regression Model: Think GenIQ Model
    105. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    106. A New Method of Decile Analysis Optimization for Database Models
    107. Multiple Catalog Mail Campaigns: Who Gets Mailed Next, and Which Catalog Should It Be?
    108. Building and Solving Response Optimization Models with the GenIQ Model
    109. Gaining Insights from Your Data: A Neoteric Machine Learning Method
    110. Data Mining for the Desktop
    111. Radically Distinctive Without Equal Predictive Model
    112. Extracting Nonlinear Dependencies: An Easy, Automatic Method
    113. 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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