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