Data defines the model by dint of genetic programming, producing the best decile table.
GenIQ Model Related Articles
Features, Book, Analytics, Solutions, and References

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Click any or all eight interesting sections with engaging topics, below: 
     1) Features,
          2) Extra-GenIQ Applications, 
               3) Book, 
                    4) Webcast, 
                         5) Analytics,  
                              6) Solutions, 
                                   7) Reference Articles, 
                                        8) Useful SAS Code.


1) Features
    1. Shakespearian Modelogue
    2. GenIQ: A Visual Introduction
    3. Value-added Benefits of GenIQ
    4. GenIQ as a Unique Data Mining Tool
    5. GenIQ Lets the Data Specify the Model
    6. GenIQs Predictive Power 
    7. GenIQ as a Data-straightener
    8. GenIQs User-friendliness 
    9. GenIQs Model is Best for Allotted Time
    10. What is Genetic Programming?
    11. GenIQs 9-step Modeling Process
    12. FAQs about GenIQ
    13. How GenIQ Works
    14. How To Use GenIQ
    15. Scoring GenIQ Models with Excel
    16. Nonrandom Words of Praise for GenIQ
    17. Random Words of Praise for GenIQ
    18. Analytical Model Development and Deployment
    19. GenIQ: Nonlinear Curve Fitter
    20. GenIQ: OLS Curve Fitter
    21. A Method for Moderating Outliers, Instead of Discarding Them
    22. GenIQ-enhanced Regression Model
    23. GenIQ-enhanced/Data-reused Regression
    24. Real World Data are Dirty: Data Cleaning and the "Noise" Problem
    25. Statistical Modeling Problems: Nonissue for GenIQ
    26. Overfitting: Old Problem, New Solution
    27. Data Cleaning is Not Completed Until the “Noise” is Eliminated


2) Extra-GenIQ Applications

    1. A Database Marketing Regression Model that Maximizes Cum Lift
    2. Overfitting: Old Problem, New Solution
    3. Data Cleaning is Not Completed Until the “Noise” is Eliminated
    4. GenIQ-enhanced Regression Model
    5. GenIQ-enhanced/Data-reused Regression
    6. A Method for Moderating Outliers, Instead of Discarding Them
    7. How to Make the Best Credit Score Even Better
    8. GenIQ: Nonlinear Curve Fitter
    9. GenIQ: OLS Curve Fitter
    10. Real World Data are Dirty: Data Cleaning and the "Noise" Problem
    11. Optimizing Website Content via the Taguchi Method
 
3) Book
 
Statistical Modeling and Analysis for Database Marketing:
Effective Techniques for Mining Big Data (click title) -
Bruce Ratner, Ph.D.

NEW! Statistical and Machine-Learning Data Mining: Techniques for Better Modeling and Analyzing Big Data
Chapter 5 - Abstract
Chapter 6 - Abstract
Chapter 13 - Abstract


4) Webcast


5) Analytics 

    1. Building a Model to Insure a TEST Group Outperforms a CONTROL Group
    2. The Uplift Model: Building a Database Model to Assess the True Impact of a Test Campaign
    3. A Data Mining Method for Moderating Outliers, Instead of Discarding Them
    4. The Originative Statistical Regression Models: Are They Too Old and Untenable?
    5. The Predictive Model: Its Reliability and Validity
    6. Life-Time Value Modeling of Big-ticket Items 
    7. Validating the Logistic Regression Model: Try Bootstrapping
    8. Regression Modeling Involves Art, Science, and Poetry Too
    9. Re-Data-Mining Your Constantly-updated Database: A Criterion for Doing So
    10. What Criteria Do You Use to Build a Model that Maximizes the Cum Lift?
    11. What Criteria Do You Use to Determine the Best Model?
    12. Top Five Statistical Modeling Problems: Nonissues for the Machine-learning GenIQ Model
    13. Statistical vs. Machine-Learning Data Mining
    14. CHAID-based Data Mining for Paired-Variable Assessment
    15. The Missing Statistic in the Decile Table: The Confidence Interval
    16. The Importance of Straight Data: Simplicity and Desirability for Good Model Building Practice
    17. The Paradox of Overfitting
    18. Building a Database Model to Assess the True Impact of a Test Campaign
    19. To Fit or Not to Fit Data to a Model
    20. Assessing the Predictiveness of a Classification Model: Traditional vs. Modern Methods 
    21. Two-by-Two Classification and Decile Tables - A Comparison
    22. Genetic vs. Statistic Regression Models - A Comparison
    23. Your Customers are Talking: Are You Listening?
    24. Is Not a Response-Model Tree a Response-Model Tree by Any Other Name?
    25. Interpretation of Coefficient-free Models
    26. Social Network Analysis, Social Media Data, and Text Mining to Boost Business Intelligence 
    27. Predictive Modeling Using Real-time Data
    28. Data Mining Quiz - II
    29. Data Mining Quiz
    30. How Large a Sample is Required to Build a Database Response Model?
    31. CHAID: Nine Inventive, Utile Applications Beyond Its Original Intent
    32. Response-Approval Model: An Effective Approach for Implementation
    33. Data Mining: Illustration of the Pythagorean Theorem
    34. Stepwise is a Problematic Method for Variable Selection in Regression: Alternative Methods are Available
    35. What If There Were No Significance Testing?
    36. A Simple Method for Assessing Linear Trend and Seasonality Components in Database Models
    37. Variable Selection Methods in Regression: Ignorable Problem, Outing Notable Solution
    38. A New CRM Method for Identifying High-value Responders
    39. Predicting the Quality of Your Statistical Regression Models
    40. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    41. What is the GenIQ Model?
    42. Linear Probability, Logit, and Probit Models: How Do They Differ?  
    43. When Data Are Too Large to Handle in the Memory of Your Computer
    44. A New Method of Modeling Missing Data: Deliverance of Discarded, Incomplete Cases
    45. Predicting Share of Wallet without Survey Data
    46. Variable Selection Methods in Regression: Many Statisticians Know Them, But Few Know They Produce Poorly Performing Models
    47. Statistical Modelers and Data Miners: Variable Selection, Data Mining Paradigm, Optimal Decile Table, and more ...
    48. The GenIQ Model: Data-defined, Data Mining, Variable Selection, and Decile Optimization
    49. Data Mining: An Ill-defined Concept
    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
    114. Handling Qualitative Attributes: Upgrading Discrete Heritable Information
 
6) Solutions

    1. Social Marketing Intelligence for Sweeping Improvement in Marketing Campaigns
    2. Model Selection for Credit Card Profitable Approval
    3. Your Customers are Talking: Are You Listening?
    4. Controlling Credit Risk: Building a Not-Yet Popular Forecasting Model
    5. Improve Marketing ROI: Predictive Analytics Using Real-time Data
    6. A Customer Intelligence Model: A New Approach to Gain Customer Insight
    7. Marketing Optimization: Regression-tree Approach for Outbound Campaigns
    8. Identifying Your Best Customers: Descriptive, Predictive and Look-Alike Profiling
    9. Latent Class Analysis and Modeling: A Pharmaceutical Case Study
    10. Subprime Lender Short Term Loan Models for Credit Default and Exposure
    11. Credit Risk Modeling – A Machine Learning Approach
    12. Finding Tax Cheaters Easily
    13. CRM Success with Data Mining
    14. Retail Revenue Optimization: Accounting for Profit-eating Markdowns
    15. Nonprofit Modeling: Remaining Competitive and Successful
    16. Detecting Fraudulent Insurance Claims: A Machine Learning Approach
    17. Demand Forecasting for Retail: A Genetic Approach
    18. CRM: Cross-Sell and Up-Sell to Improve Response Rates and Increase Revenue
    19. Performance Management: Improve It via Machine Learning
    20. Risk Management for the Insurance Industry: A Machine Learning Approach
    21. Credit Scoring: A New Approach to Control Risk
    22. Customer-Value Based Segmentation: An Overview
    23. Trigger Marketing: Predicting the Next Best Offer to Give Customers
    24. Marketing Mix Model: Right Offer, Right Time, and Right Channel
    25. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    26. A Machine Learning Approach to Conjoint Analysis
    27. Subprime Borrower Market: Building a Subprime Lender Scoring Model for a Homogeneous Segment
    28. The Financial Services Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
    29. Retail Revenue Optimization: A Model-free Approach
    30. Fraud Detection: Beyond the Rules-Based Approach
    31. Product Positioning: Predicting the Next Best Offer to Give Customers
    32. Marketing Mix Model: A Genetic Approach
    33. Optimizing Customer Loyalty
    34. Telecommunication Fraud Reduction: Analytical Approaches
    35. The Banking Industry Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
    36. Fundraising Modeling: Competitive and Successful

7) Reference Articles

    1. Accidental Statistician: Who Can Befitted of a Self-described Caption?
    2. A Dozen Statisticians, A Dozen Outcomes
    3. A Popular Statistical Term Coined with the Formula X's Y
    4. "Few things are harder to put up with than the annoyance of a good (statistics) example"
    5. Survival of the Fittest: Who Coined It, and When?
    6. How Does Spearman's Coefficient Relate to Pearson's Coefficient?
    7. Calculating the Average Correlation Coefficient: Why?
    8. What If There Were No Significance Testing?
    9. Predicting the Quality of Your Statistical Regression Models
    10. Pop Quiz on Pi
    11. Linear Probability, Logit, and Probit Models: How Do They Differ?
    12. How To Bootstrap
    13. The Correlation Coefficient: Definition
    14. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    15. Logistic Regression: Definition
    16. CHAID: Its Original Intent
    17. CHAID for Uncovering Relationships: A Data Mining Tool
    18. Market Segmentation: Defining Target Markets with CHAID
    19. The Working Concepts for Building a Database Acquisition Model
    20. The Working Concepts for Building a Database Retention Model
    21. The Working Concepts for Building a Database Attrition Model
    22. Optimizing Website Content via the Taguchi Method
    23. Sensitivity Analysis for Database Marketing Models
    24. Creating a SAS8 Dataset from a SAS9 Dataset
    25. A Very Automatic Coding of Dummy Variables
    26. Einstein: A Clever, Self-taught Statistician
    27. Data Mining Paradigm: Historical Perspective
    28. Data Mining: An Ill-defined Concept
    29. Pythagoras: Everyone Knows His Famous Theorem, but Not Who Discovered It One Thousand Years before Him
    30. Karl Pearson: Everybody Knows His Correlation Coefficient, but Not How “Close” the Binomial Distribution is to a Normal Distribution
    31. Florence Nightingale: You Know Her as the Pioneer of Modern Nursing, But as a Passionate Statistician!
    32. Statistical Terms: Who Coined Them, and When?
    33. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    34. Different Data, Identical Regression Models: Which Model is Better?
    35. The Importance of Straight Data: For Simplicity, Desirable for Good Modeling
    36. The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They?
    37. A Trilogy of “Item” Biographies of Our Favorite Statisticians
    38. HELP! I Need Somebody, Not Just Anybody ...
    39. Do-It-Yourself Method for Finding the Square Root of 2
    40. Given an Irrational Number, are the Digits after the Decimal Point Random?
    41. Given the Irrational Number Pi, are the Digits after the Decimal Point Random? 
    42. What is the Probability of a Miracle?
    43. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    44. Handling Qualitative Attributes: Upgrading Discrete Heritable Information
       

8) Useful SAS Code
    1. Calculating the Average Correlation Coefficient: Why?
    2. Creating a Bootstrap Sample
    3. A Very Automatic Coding of Dummy Variables
    4. Collapsing Multiple Observations into a Single Observation
    5. Spreading Mutliple (Monthly) Observations into a Single Observation
    6. Spreading and Summing Multiple (Monthly) Obserations into a Single Observation
For more information about these articles, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT-1; or e-mail at br@dmstat1.com.
Sign-up for a free GenIQ webcast: Click here.


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