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

If you would like to be notified when
new articles are added, please click here.


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


  

Book
 
Statistical Modeling and Analysis for Database Marketing:
Effective Techniques for Mining Big Data (4th printing) -
Bruce Ratner, Ph.D.


Webcast
For Regression Modelers and Data Miners: Online Demo of the GenIQ Model©


Articles
    1. Overfitting: Old Problem, New Solution
    2. Data Cleaning is Not Completed Until the “Noise” is Eliminated
    3. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    4. Real World Data are Dirty: Data Cleaning and the "Noise" Problem
    5. GenIQ: For Modelers Who Daringly Consider a Different Model –
    6. The Most Compelling Illustration of the GenIQ Model
    7. A Most Compelling Illustration of the GenIQ Model
    8. GenIQ Lets the Data Specify the Model
    9. Data Mining Using Genetic Programming
    10. GenIQ-enhanced Regression Model 
    11. GenIQ-enhanced/Data-reused Regression
    12. GenIQ: Nonlinear Curve Fitter
    13. GenIQ: OLS Curve Fitter
    14. A Method for Moderating Outliers, Instead of Discarding Them
    15. Building Statistical Regression Models: Straight Data are Necessary
    16. Logistic Regression versus Machine Learning Regression
    17. Ordinary Regression versus Machine Learning Regression 
    18. The GenIQ Model: FAQs
    19. Interpreting Model Performance: Use the “Smart” Decile Analysis
    20. Predictor Variable Importance: Multicollinearity is Not a Problem for a Genetic Regression Model
    21. Dummy Variables: The Problem and Its Solution
    22. Finding the Best Variables for Database Marketing Models
    23. Decile Analysis Primer: Cum Lift for Response Model
    24. Maximizing the Lift in Database Marketing
    25. When Statistical Model Performance is Poor: Try Something New, and Try It Again
    26. A Hybrid Statistics-Machine Learning Paradigm for Database Response Modeling
    27. Tukey's Bulging Rule: Why Use It, and What to Do When It Fails
    28. Tukey's Bulging Rule for Straightening Data
    29. Modeling a Skewed Distribution with Many Zero Values
    30. A New Jackknife Method: 3-in-1 Tool for Variable Selection, Data Mining and Model Building
    31. A Genetic Model to Identify Titanic Survivors
    32. Statistics versus Machine Learning: A Significant Difference for Database Response Modeling
    33. The Genetic Programming Engine that Does: Data Specify the Model, Not Fit Data to a Model
    34. GenIQ-Parkinson's Law: The GenIQ Model Expands to Fill the Time Available for Model Completion 
    35. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    36. A Genetic Imputation Method for Database Modeling
    37. Missing Value Analysis: A Machine-learning Approach
    38. A Genetic Logistic Regression Model: A Model-free Approach to Identifying Responders to a CRM Solicitation
    39. Predictive Analytics Now Accessible to Excel Spreadsheet Users: GenIQ Model Software with an Excel Toolbar
    40. An Alternative Response Model
    41. Analysis and Modeling for Today's Data
    42. Using the GenIQ Model to Insure the Validation of a Model is Unbiased
    43. Gain of a Predictive Information Advantage: Data Mining via Evolution
    44. Response-Approval Model: An Effective Approach for Implementation
    45. Marketing Optimization Model: A Genetic Approach 
    46. Binary Logistic Regression: A Model-free Approach
    47. Ordinal Logistic Regression: A Model-free Approach
    48. Multinomial Logistic Regression: A Model-free Approach
    49. Quantile Regression: Model-free Approach
    50. Rethink The Regression Model: Think GenIQ Model
    51. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    52. A New Method of Decile Analysis Optimization for Database Models
    53. Multiple Catalog Mail Campaigns: Who Gets Mailed Next, and Which Catalog Should It Be?
    54. Building and Solving Response Optimization Models with the GenIQ Model
    55. Gaining Insights from Your Data: A Neoteric Machine Learning Method
    56. Data Mining for the Desktop
    57. Radically Distinctive Without Equal Predictive Model
    58. Extracting Nonlinear Dependencies: An Easy, Automatic Method
    59. Retail Revenue Optimization: Accounting for Profit-eating Markdowns
 

Solutions

    1. Subprime Lender Short Term Loan Models for Credit Default and Exposure
    2. Credit Risk Modeling – A Machine Learning Approach
    3. Finding Tax Cheaters Easily
    4. CRM Success with Data Mining
    5. Retail Revenue Optimization: Accounting for Profit-eating Markdowns
    6. Nonprofit Modeling: Remaining Competitive and Successful
    7. Detecting Fraudulent Insurance Claims: A Machine Learning Approach
    8. Demand Forecasting for Retail: A Genetic Approach
    9. CRM: Cross-Sell and Up-Sell to Improve Response Rates and Increase Revenue
    10. Performance Management: Improve It via Machine Learning
    11. Risk Management for the Insurance Industry: A Machine Learning Approach
    12. Credit Scoring: A New Approach to Control Risk
    13. Customer-Value Based Segmentation: An Overview
    14. Trigger Marketing: Predicting the Next Best Offer to Give Customers
    15. Marketing Mix Model: Right Offer, Right Time, and Right Channel
    16. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    17. A Machine Learning Approach to Conjoint Analysis
    18. Subprime Borrower Market: Building a Subprime Lender Scoring Model for a Homogeneous Segment
    19. The Financial Services Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
    20. Retail Revenue Optimization: A Model-free Approach
    21. Fraud Detection: Beyond the Rules-Based Approach
    22. Product Positioning: Predicting the Next Best Offer to Give Customers
    23. Marketing Mix Model: A Genetic Approach
    24. Optimizing Customer Loyalty
    25. Telecommunication Fraud Reduction: Analytical Approaches
    26. The Banking Industry Problem-Solution: Reduce Costs, Increase Profits by Data Mining and Modeling
    27. Fundraising Modeling: Competitive and Successful


References Articles

    1. The Correlation Coefficient: Definition
    2. Calculating the Average Correlation Coefficient
    3. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    4. Logistic Regression: Definition
    5. CHAID: Its Original Intent
    6. CHAID for Uncovering Relationships: A Data Mining Tool
    7. Market Segmentation: Defining Target Markets with CHAID
    8. The Working Concepts for Building a Database Acquisition Model
    9. The Working Concepts for Building a Database Retention Model
    10. The Working Concepts for Building a Database Attrition Model
    11. Optimizing Website Content via the Taguchi Method
    12. Sensitivity Analysis for Database Marketing Models
    13. Creating a SAS8 Dataset from a SAS9 Dataset
    14. Einstein: A Clever, Self-taught Statistician
    15. Data Mining Paradigm: Historical Perspective
    16. Karl Pearson: Everybody Knows His Correlation Coefficient, but Not How “Close” the Binomial Distribution is to a Normal Distribution
    17. Florence Nightingale: You Know Her as the Pioneer of Modern Nursing, But as a Passionate Statistician!
    18. Statistical Terms: Who Coined Them, and When?
    19. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    20. Different Data, Identical Regression Models: Which Model is Better?
    21. The Importance of Straight Data: For Simplicity, Desirable for Good Modeling
    22. The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They?
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.


DM STAT-1 CONSULTING / br@dmstat1.com
574 Flanders Drive / North Woodmere, NY 11581 / U S A
Voice 1-516-791-3544 / Fax 1-516-791-5075
Toll Free 1 800 DM STAT-1