Data defines the model by dint of genetic programming, producing the best decile table.


GenIQ Model Related Articles
Features, Books, 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) Books, 
                    4) Webcast
                         5) Analytics,  
                              6) Solutions, 
                                   7) Reference Articles, 
                                        8) Useful SAS Code,
                                        9) Third Edition.                       
                                                                                



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 
    12. GenIQ Text-Miner Software
 
3) Books
 
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data 

NEW!
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data  



4) Webcast


5) Analytics 

    1. Profile Analysis of Any Regression-based Model
    2. Opening the Dataset: A Twelve-Step Program for Dataholics
    3. Opening the Dataset: Confession of a Dataholic
    4. Market Segmentation: An Easy Way to Understand the Segments
    5. The Statistical Golden Rule: Measuring the Art and Science of Statistical Practice
    6. What is Your First Data Step?
    7. One Pound of Pennies: The Correlation Between the Mean Value of Pennies and the Skew of the Year of Mint
    8. Statistically Confident: Asking a Dumb Question
    9. Big Data and Learning Analtyics: A Recommended Resource
    10. Stevens’ Four Scales of Measurement: The Addition of a New Scale
    11. Apple and Orange Comparison: Statistically Fruitless or Fruitful?
    12. Data Mining and the Golden Gut: Complementary, Supplementary or Mutually Exclusive?
    13. Re-Modeling the Coupon Redemption Decision
    14. Big Data, Schmea Data, It Still Boils Down to the Super Six Statistics
    15. Book-Mash: Random Stacking of Statistics Books
    16. A Glass of Water vs. A Can of Trash: What Say You, Half-Empty or Half-Full?
    17. Wouldn’t It Be Nice to Have a Regression Technique that Builds the Best Model Possible Within an Allotted Time?
    18. Life-Time Value Modeling of Big-ticket Items
    19. My Statistics Floater: One-Sample Test for Two Mutually-Exclusive Proportions
    20. Zero-Inflated Regression: Modeling a Distribution with a Mass at Zero
    21. The Originative Regression Models: Are They too Old and Untenable?
    22. Outperforming a Multi-Level Classification Model Whose Chance Performance is Large
    23. Bruce Ratner's Statistical and Machne-Learning Data Mining Book is on Intel's Recommended Reading List
    24. Building a Multi-Level Classification Model to Simultaneously Maximize Decile Tables for Each Level, Not the Traditional Confusion Matrix
    25. Principal Component Analysis of Yesterday and Today
    26. Building a Model to Insure a TEST Group Outperforms a CONTROL Group
    27. The Uplift Model: Building a Database Model to Assess the True Impact of a Test Campaign
    28. A Data Mining Method for Moderating Outliers, Instead of Discarding Them
    29. The Originative Statistical Regression Models: Are They Too Old and Untenable?
    30. The Predictive Model: Its Reliability and Validity
    31. Life-Time Value Modeling of Big-ticket Items 
    32. Validating the Logistic Regression Model: Try Bootstrapping
    33. Regression Modeling Involves Art, Science, and Poetry Too
    34. Re-Data-Mining Your Constantly-updated Database: A Criterion for Doing So
    35. What Criteria Do You Use to Build a Model that Maximizes the Cum Lift?
    36. What Criteria Do You Use to Determine the Best Model?
    37. Top Five Statistical Modeling Problems: Nonissues for the Machine-learning GenIQ Model
    38. Statistical vs. Machine-Learning Data Mining
    39. CHAID-based Data Mining for Paired-Variable Assessment
    40. The Missing Statistic in the Decile Table: The Confidence Interval
    41. The Importance of Straight Data: Simplicity and Desirability for Good Model Building Practice
    42. The Paradox of Overfitting
    43. Building a Database Model to Assess the True Impact of a Test Campaign
    44. To Fit or Not to Fit Data to a Model
    45. Assessing the Predictiveness of a Classification Model: Traditional vs. Modern Methods 
    46. Two-by-Two Classification and Decile Tables - A Comparison
    47. Genetic vs. Statistic Regression Models - A Comparison
    48. Your Customers are Talking: Are You Listening?
    49. Is Not a Response-Model Tree a Response-Model Tree by Any Other Name?
    50. Interpretation of Coefficient-free Models
    51. Social Network Analysis, Social Media Data, and Text Mining to Boost Business Intelligence 
    52. Predictive Modeling Using Real-time Data
    53. Data Mining Quiz - II
    54. Data Mining Quiz
    55. How Large a Sample is Required to Build a Database Response Model?
    56. CHAID: Nine Inventive, Utile Applications Beyond Its Original Intent
    57. Response-Approval Model: An Effective Approach for Implementation
    58. Data Mining: Illustration of the Pythagorean Theorem
    59. Stepwise is a Problematic Method for Variable Selection in Regression: Alternative Methods are Available
    60. What If There Were No Significance Testing?
    61. A Simple Method for Assessing Linear Trend and Seasonality Components in Database Models
    62. Variable Selection Methods in Regression: Ignorable Problem, Outing Notable Solution
    63. A New CRM Method for Identifying High-value Responders
    64. Predicting the Quality of Your Statistical Regression Models
    65. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    66. What is the GenIQ Model?
    67. Linear Probability, Logit, and Probit Models: How Do They Differ?  
    68. When Data Are Too Large to Handle in the Memory of Your Computer
    69. A New Method of Modeling Missing Data: Deliverance of Discarded, Incomplete Cases
    70. Predicting Share of Wallet without Survey Data
    71. Variable Selection Methods in Regression: Many Statisticians Know Them, But Few Know They Produce Poorly Performing Models
    72. Statistical Modelers and Data Miners: Variable Selection, Data Mining Paradigm, Optimal Decile Table, and more ...
    73. The GenIQ Model: Data-defined, Data Mining, Variable Selection, and Decile Optimization
    74. Data Mining: An Ill-defined Concept
    75. GenIQ: A Visual Introduction
    76. Overfitting: Old Problem, New Solution
    77. Genetic Data Mining: The Correlation Coefficient
    78. Data Cleaning is Not Completed Until the “Noise” is Eliminated
    79. How to Make the Best Credit Score Even Better
    80. Multivariate Regression Trees: An Alternative Method
    81. "Grand" words (1000) about the GenIQ Model
    82. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    83. Real World Data are Dirty: Data Cleaning and the "Noise" Problem
    84. GenIQ: For Modelers Who Daringly Consider a Different Model –
    85. The Most Compelling Illustration of the GenIQ Model
    86. A Most Compelling Illustration of the GenIQ Model
    87. GenIQ Lets the Data Specify the Model
    88. Data Mining Using Genetic Programming
    89. GenIQ-enhanced Regression Model
    90. GenIQ-enhanced/Data-reused Regression
    91. GenIQ: Nonlinear Curve Fitter
    92. GenIQ: OLS Curve Fitter
    93. A Method for Moderating Outliers, Instead of Discarding Them
    94. Building Statistical Regression Models: Straight Data are Necessary
    95. Logistic Regression versus Machine Learning Regression
    96. Ordinary Regression versus Machine Learning Regression
    97. The GenIQ Model: FAQs
    98. Interpreting Model Performance: Use the “Smart” Decile Analysis
    99. Predictor Variable Importance: Multicollinearity is Not a Problem for a Genetic Regression Model
    100. Dummy Variables: The Problem and Its Solution
    101. Finding the Best Variables for Database Marketing Models
    102. Decile Analysis Primer: Cum Lift for Response Model
    103. Maximizing the Lift in Database Marketing
    104. When Statistical Model Performance is Poor: Try Something New, and Try It Again
    105. A Hybrid Statistics-Machine Learning Paradigm for Database Response Modeling
    106. Tukey's Bulging Rule: Why Use It, and What to Do When It Fails
    107. Tukey's Bulging Rule for Straightening Data
    108. Modeling a Skewed Distribution with Many Zero Values
    109. A New Jackknife Method: 3-in-1 Tool for Variable Selection, Data Mining and Model Building
    110. A Genetic Model to Identify Titanic Survivors
    111. Statistics versus Machine Learning: A Significant Difference for Database Response Modeling
    112. The Genetic Programming Engine that Does: Data Specify the Model, Not Fit Data to a Model
    113. GenIQ-Parkinson's Law: The GenIQ Model Expands to Fill the Time Available for Model Completion
    114. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    115. A Genetic Imputation Method for Database Modeling
    116. Missing Value Analysis: A Machine-learning Approach
    117. A Genetic Logistic Regression Model: A Model-free Approach to Identifying Responders to a CRM Solicitation
    118. Predictive Analytics Now Accessible to Excel Spreadsheet Users: GenIQ Model Software with an Excel Toolbar
    119. An Alternative Response Model
    120. Analysis and Modeling for Today's Data
    121. Using the GenIQ Model to Insure the Validation of a Model is Unbiased
    122. Gain of a Predictive Information Advantage: Data Mining via Evolution
    123. Response-Approval Model: An Effective Approach for Implementation
    124. Marketing Optimization Model: A Genetic Approach
    125. Binary Logistic Regression: A Model-free Approach
    126. Ordinal Logistic Regression: A Model-free Approach
    127. Multinomial Logistic Regression: A Model-free Approach
    128. Quantile Regression: Model-free Approach
    129. Rethink The Regression Model: Think GenIQ Model
    130. Building a CRM Model for Identifying Profitable Leads: The Genetic Contact-Profit Model
    131. A New Method of Decile Analysis Optimization for Database Models
    132. Multiple Catalog Mail Campaigns: Who Gets Mailed Next, and Which Catalog Should It Be?
    133. Building and Solving Response Optimization Models with the GenIQ Model
    134. Gaining Insights from Your Data: A Neoteric Machine Learning Method
    135. Data Mining for the Desktop
    136. Radically Distinctive Without Equal Predictive Model
    137. Extracting Nonlinear Dependencies: An Easy, Automatic Method
    138. Retail Revenue Optimization: Accounting for Profit-eating Markdowns
    139. 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. Statisticians Have a Bad Habit
    2. Power of Thought
    3. Accidental Statistician: Who Can Befitted of a Self-described Caption?
    4. A Dozen Statisticians, A Dozen Outcomes
    5. A Popular Statistical Term Coined with the Formula X's Y
    6. "Few things are harder to put up with than the annoyance of a good (statistics) example"
    7. Survival of the Fittest: Who Coined It, and When?
    8. How Does Spearman's Coefficient Relate to Pearson's Coefficient?
    9. Calculating the Average Correlation Coefficient: Why?
    10. What If There Were No Significance Testing?
    11. Predicting the Quality of Your Statistical Regression Models
    12. Pop Quiz on Pi
    13. Linear Probability, Logit, and Probit Models: How Do They Differ?
    14. How To Bootstrap
    15. The Correlation Coefficient: Definition
    16. Genetic Data Mining Method for the Proper Use of the Correlation Coefficient
    17. Logistic Regression: Definition
    18. CHAID: Its Original Intent
    19. CHAID for Uncovering Relationships: A Data Mining Tool
    20. Market Segmentation: Defining Target Markets with CHAID
    21. The Working Concepts for Building a Database Acquisition Model
    22. The Working Concepts for Building a Database Retention Model
    23. The Working Concepts for Building a Database Attrition Model
    24. Optimizing Website Content via the Taguchi Method
    25. Sensitivity Analysis for Database Marketing Models
    26. Creating a SAS8 Dataset from a SAS9 Dataset
    27. A Very Automatic Coding of Dummy Variables
    28. Einstein: A Clever, Self-taught Statistician
    29. Data Mining Paradigm: Historical Perspective
    30. Data Mining: An Ill-defined Concept
    31. Pythagoras: Everyone Knows His Famous Theorem, but Not Who Discovered It One Thousand Years before Him
    32. Karl Pearson: Everybody Knows His Correlation Coefficient, but Not How “Close” the Binomial Distribution is to a Normal Distribution
    33. Florence Nightingale: You Know Her as the Pioneer of Modern Nursing, But as a Passionate Statistician!
    34. Statistical Terms: Who Coined Them, and When?
    35. Historical Notes on the Two Most Popular Prediction Models, and One Not-yet Popular Model
    36. Different Data, Identical Regression Models: Which Model is Better?
    37. The Importance of Straight Data: For Simplicity, Desirable for Good Modeling
    38. The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They?
    39. A Trilogy of “Item” Biographies of Our Favorite Statisticians
    40. HELP! I Need Somebody, Not Just Anybody ...
    41. Do-It-Yourself Method for Finding the Square Root of 2
    42. Given an Irrational Number, are the Digits after the Decimal Point Random?
    43. Given the Irrational Number Pi, are the Digits after the Decimal Point Random? 
    44. What is the Probability of a Miracle?
    45. Confusion Matrix: Perhaps Confusing, but Definitely Biased
    46. Handling Qualitative Attributes: Upgrading Discrete Heritable Information
       

8) Useful SAS Code

    1. SAS Code for Performance of Model vs. Chance Model
    2. SAS Code for K-Means Clustering
    3. SAS Code for Changing Prefix of Variable Names
    4. SAS Code for Determining Number of Variables in a Dataset
    5. SAS Code for Bootstrapped Decile Analysis
    6. SAS Code for Normalizing Variable to Lie Within [0, 1]
    7. SAS Code for Direction of Correlates of Varclus
    8. SAS Code for Smoothplot
    9. SAS Code for Finding Mid-Spread of Two Variables
    10. SAS RENAME Coding
    11. SAS Code for WHERE Statement
    12. SAS Code for Removing All Variable Labels
    13. SAS Code for Proc Corr with WITH-Variable, Output Vertical
    14. SAS Code for Calculation of Average Correlation Among Variables
    15. Splitting a Dataset Between Numeric and Character Variables
    16. SAS Code for Finding Frequent Variables Across Files
    17. SAS Code for Listing Predictor Variables in a Title
    18. SAS Import Wizard Needs a Little Magic
    19. SAS Code for INPUT and PUT Functions
    20. Decile Analysis - the Basic
    21. Decile Analysis of X1 and X2 Based on Model Estimate
    22. Decile Analysis - Sales
    23. SAS Code for Running Medians of Three
    24. SAS Code for Creating a Trend Dataset
    25. SAS Code for Appending a Calculated Value
    26. SAS Code for Ranking Predictors
    27. SAS Code for Proc Tabulate - basic 
    28. SAS Code for Converting a Num to Char Variable, and Back
    29. SAS Code for Reshaping 3x5 Dataset into 5x3 Dataset
    30. SAS Code for Renaming A Variable's Case
    31. SAS Code for Dots to Zeros
    32. SAS Code for Basic ODS
    33. Scoring A Principal Component
    34. Scoring An Oblique Principal Component
    35. Scoring and Appending the Assigned-Cluster from An Oblique Principal Component 
    36. Creating a Variable List of Big Data
    37. Calculating a Weight Variable for the Number of Repeated Values of a Variable
    38. Creating Dummy Variables Corresponding to Values of Character Variables
    39. Creating Count Variables Corresponding to Values of Character Variables 
    40. Creating Time-On-File Variable
    41. Creating a Numeric Date (mmddyy) to a SAS Date
    42. Calculating the Average Correlation Coefficient: Why?
    43. Creating a Bootstrap Sample
    44. A Very Automatic Coding of Dummy Variables
    45. Collapsing Multiple Observations into a Single Observation
    46. Spreading Mutliple (Monthly) Observations into a Single Observation
    47. Spreading and Summing Multiple (Monthly) Obserations into a Single Observation

9)Third Edition
 
1. Chapter 8 – Market Share Estimation: Data Mining for an Exception Case






    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.
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