|
's
flagship product GenIQ Model Software is an implementation of the GenIQ
Model, which explicitly maximizes the expected response and profit from
solicitations. The GenIQ Model is an assumption-free, nonparametric
methodology based on Darwin's Principle of Survival of the Fittest, and
natural genetic operations - namely, genetic programming. The
genetic-based GenIQ-Response and GenIQ-Profit modules offer a clear
advantage over logistic and ordinary regression methods, whose
performance is dependent on theoretical assumptions and data
restrictions. The GenIQ Model determines the best set of predictors
based on a simultaneous and virtually unbiased assessment of all
variables, an achievement not possible with current methods. For the
unique value-added benefits of the GenIQ Software click here. In addition, the GenIQ Model
serves as an excellent data
mining technique. Perhaps, GenIQ is the Easiest Method of Building
a Database Response Model.
Experience for yourself the power of the GenIQ Model -
to any of your past or present modeling projects. I
guarantee it will make you rethink regression-based modeling forever.
If not, I will give you a free copy of my latest
book Statistical Modeling and
Analysis for Database Marketing: Effective Techniques for Mining Big
Data. Request the
full-featured GenIQ Model Software demo to rebuild or build your own
GenIQ Model to compare to your past models, or assess the ease-of-use
and predictiveness of GenIQ on your current project, respectively.
(Paradoxically, the less you know about statistics the better
candidate you are forGenIQ.) If you prefer, provide me with
the datasets for building and validating one of your models. After
employing GenIQ to your data, I will provide you a complete paper-trail
of the procedure and report the new model results. Of course, I will
sign a nondisclosure agreement and accept your data completely
disguised, if you desire. If you're interested in perhaps the easiest
and most predictive method of building a database model, feel free to
call me at 1.516.791.3544 (1.800. DM STAT1), or email at br@dmstat1.com.
, the leading firm for analysis
and modeling in the DM Space (direct/database marketing, CRM, and data
mining/knowledge discovery), and a premier developer of software tools
that rely on machine learning (ML) technologies, have collaborated in
the development of powerful, yet user-friendly applications of hybrid
statistics-ML models.
The new partnership has refined the statistical
models used in DM to make them more powerful. Thanks to the flexibility
of ML computation, our new models explicitly address DM objectives - a
major improvement over their traditional counterparts. The new models
are efficiently estimated with algorithms that harness the power of the
desktop computer.
The GenIQ Model can comfortably accomondate BIG data consisting of
hundreds of thousands of observations. For BIGGER data consisting of a
million or more observations, has an arrangement with Dr. John
R. Koza of Stanford University (the inventor of genetic programming) to
implement the GenIQ Model on his Cluster Computer System comprised of
1,000 Pentium II 350-MHz personal computers.
Database Marketing Analysis, Mining and Modeling
GenIQ is an automatic model generation tool that can significantly
improve the response rates (as measured from any binary target
variable) and profitability (as measured from any continuous target
variable) of DM solicitations. Using
genetic modeling techniques, GenIQ concentrates responders and high
profit customers into the upper deciles of its model scores.
GenIQ addresses the primary goals in the DM Space by focusing on
aggregates instead of individual scores to obtain (in the upper
deciles):
- as many responses as possible, and
- as much profit as possible.
By explicitly maximizing the cumulative lift in the upper
deciles, GenIQ builds high-performance response and profit models that
significantly outperform other methods. In contrast, linear and
logistic regression, discriminant analysis and neural nets achieve
marketing goals indirectly. Rather than maximizing decile
response and profit, other methods minimize squared error and derive
scores that are not optimized for the problem. Recent trials show that
GenIQ can boost cumulative lift in the top two to four deciles by 10%
to 25% over traditional models.
What is Genetic Modeling?
Genetic model generation is based on the Darwinian ideas of "survival
of the fittest" and natural selection. The process begins with a
fitness function (in GenIQ, populating the upper deciles with as much
response or profit as possible) and a set of user-selectable
mathematical and logical operators. A first generation of as many as
10,000 models is randomly generated using the operators available; the
"fitness" of each model is evaluated using training data.
A second generation of models is then created through mating,
reproduction (copying) and mutation (random changes in some models).
When models "mate," the offspring are mixtures of the parents, with
each parent contributing components to the child. The frequency with
which a model mates or is copied is a function of its fitness score -
how well it fills the upper deciles appropriately. After a suitable
number of generations (typically several hundred), the forces of
natural selection yield a number of models superbly adapted to the
objective.
For a technical discussion of genetic modeling (programming) click here.
Graphical User Interface
GenIQ guides the user through all phases of importing data and setting
up the model. The model parameters - population size, genetic
functions, and breeding (reproduction, crossover, mutation)
characteristics - are set to easily modified intelligent defaults.
Model evolution is fully automatic and can be paused at any time to
assess the best-of-generation solution, alter breeding characteristics
or change the genetic functions. Graphical displays summarize
characteristics of the fully-evolved model, which can be easily
exported to C, SAS®, SPSS®, Visual Basic, or Microsoft Excel
for deployment.
Applications
Although the fitness functions in GenIQ optimize direct marketing
outcomes, GenIQ can be used for a much broader class of problems. GenIQ
is a tool of choice whenever concentrating specific records into
well-defined groups is needed. For example, in identifying fraud, risky
credit prospects, customers most likely to go bankrupt, or home owners
most likely to refinance their mortgages, the objectives would be well
served by the GenIQ objective. Advantages of GenIQ include:
- Automatic variable selection
- Automatic model building
- A selection of alternative models to choose from
- An option to favor smaller models
- Automatic testing on a holdout sample
- Export of any model to various source code formats
(including SAS®)
To run GenIQ a user only needs to specify a training database and
select a target variable. Using broadly applicable defaults, GenIQ will
do everything else: reserve a fraction of the data for testing, select
a set of model construction operators, set breeding parameters,
determine how many generations to evolve, and then run. When the run is
complete (or any time during the evolution) the analyst can examine any
of the models of the current generation in graphical source code form.
Experienced users will want to experiment with different program
settings. For example, GenIQ provides an extensive collection of
functions and operators from which models are built up, including
arithmetic, trigonometric, logical, and fuzzy functions.
By default, only a core subset of these are made available for model
evolution, but knowledge of the subject matter may suggest that other
functions be allowed. The user can also define custom functions to add
to the stock of "genetic material."
System Requirements
- Windows 98SE, Windows ME, Windows 2000 Windows XP
- Very latest processor recommended
- At least 64MB of RAM
Technical Specifications
- All input files formats, including SAS®
- Numeric and character variables allowed
- Unlimited number of rows and columns
Go back to Home page.
|
|