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- 3rd editon  



4) Webcast


5) Analytics 

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

       2.
Chapter 11 - Predicting Share of Wallet without Survey Data 
       3. Chapter 19 - Market Segmentation Based on Time-Series Data Using Latent Class Analysis 
       4. Chapter 20 - Market Segmentation: An Easy Way to Understand the Segments 
       5. Chapter 21 - The Statistical Regression Model: An Easy Way to Understand the Model 
       6. Chapter 23 - Model Building with Big Complete and Incomplete Data 
       7. Chapter 27 - Decile Analysis: Perspective and Performance 
       8. Chapter 28 - Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns 
       9. Chapter 31 - Visualization of Marketing Models: Data Mining to Uncover Innards of a Model 
     10. Chapter 34 - Opening the Dataset: A Twelve-Step Program for Dataholics 
     11. Chapter 43 - Text Mining: Primer, Illustration, and TXTDM Software 

     12. Chapter 44 - Some of My Favorite Statistical Subroutines 


 








    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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