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Insight

Let the data determine appropriate model structures that capture the behavior of the response variables. Automated hypothesis generation of Evolved Analytic's Data Modeler delivers transparent human-interpretable models given analytically.

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Focus

Use Evolved-Analytic's DataModeler to automatically focus on the variables that matter. Variable importance computed using thousands of smooth global non-linear models gives robustness and insight unmatched by other state-of-the-art algorithms.

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Trust

Let trustable models with confidence metrics guide exploration and exploitation of your design space. Robust model ensembles of Evolved Analytics' Data Modeler warn when one is extrapolating to new regions and suggest future experiments.

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DataModeler Key Features

Evolved Analytics DataModeler provides a complete and integrated workflow for industrial-strength data-driven modeling that guides the user through the path from data to actions.

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DataModeler Testimonials

One of those rare products that changes the way you think

"DataModeler is one of those rare products that changes the way you think. It ends any excuse for extending an assumption of linearity in modeling beyond the domains in which it is truly appropriate."

— Prof. Seth J. Chandler, Foundation Professor of Law, Director of the Program on Law and Computation, University of Houston Law Center, U.S.A.

Much higher fidelity in matching predictions to data

"I have used DataModeler from Evolved Analytics in my work as a chemical engineer. I have found that compared to other genetic programming packages, DataModeler can give me much higher fidelity in matching predictions to data..."

— Peter Kip Mercure, PhD Chemical Engineering, The Dow Chemical Company, U.S.A.

Using DataModeler with Excellent Results

"We are using Data Modeler with excellent results in both our student projects and industrial applications for several years. It has access to the powerful symbolic and numerical calculation tools and the nearly endless visualization opportunities of Mathematica. Data Modeler substantially benefits..."

—Prof. Dr. Thomas Bartz-Beielstein, Head of CIOP Research Center, Cologne University of Applied Sciences, Cologne, Germany

Incredible out-of-the-box performance

"With DataModeler we were able to model a data set with 32 attributes and over 10,000 rows in less than an hour. The ensemble it produced was far more accurate than anything else we've seen. This is incredible out-of-the-box performance."

— Dr. Conor Ryan, Director at the Biocomputing and Developmental Systems Group in the Computer Science and Information Systems Department at the University of Limerick, Ireland.

DataModeler enabled the data to talk to us quickly

"We put DataModeler in the loop. It enabled the data to talk to us quickly and, without delay we could translate the insights to our client's perspective, and thoughtfully consider how to revise, refine and immediately iterate..."

— Una-May O'Reilly, PhD, Principal Research Scientist at CSAIL, MIT, Cambridge, MA

News, Releases, Events

DataModeler Release 8.08 (16 May)
Wednesday, May 16, 2012

We are proud to release DataModeler 8.08 (16 May 2012)! The theme of this release is a major new capabilities for metavariable identification and exploitation. A metavariable is simply a combination of variables or a transform of a variable which is useful in the developed models.

DataModeler Release 8.06 (26 January)
Thursday, January 26, 2012

The theme of this release is a significantly enhanced DataOutlierTable. DataOutlierTable function now allows DataRecordLabels to be displayed as well as other changes to improve the information display. Also changed the Input option to display the input variables to VariablesToPlot and added some flexibility and clarity to the input data display.

DataModeler Release 8.05 (7 December)
Thursday, December 8, 2011

The theme for this release is improved (and beautiful) model analysis. We have another suite of functions in development targeted at VariableContributionAnalysis; however, rather than hold things up while that gets lined out, we decided to get this out the door since the changes since the multi-core release are fairly extensive as well as practically useful. Since the variable contribution analysis tools are in the pipeline, the QuickStart, case studies and function examples have not yet...

DataModeler Release 8.02 (25 October)
Wednesday, October 26, 2011

Parallel computing support is the big feature from this release. If you have a multi-core processor, DataModeler automatically runs parallel IndependentEvolutions up to the limit imposed by either the number of cores available, or the license restriction on the number of subkernels (typically, four) which can be associated with a given master kernel. Of course, if you have a quad-core i7 processor, you can launch two master kernels and really make the fan on your machine spin.

DataModeler goes Multi-Core!
Wednesday, October 26, 2011

We are happy to announce that Evolved Analytics' DataModeler now fully supports multi-core computing. This increases the robustness of computations and saves time. So good to see all the cores running!

Welcome to Evolved Analytics!

The mission of Evolved Analytics is to incorporate real world into nonlinear modeling. Our technology complements classical multi-variate data analysis and feature selection, statistical learning theory and statistical inference, design of experiments and classical non-linear regression.

We solve real-world problems targeted at data-driven understanding of complex, unknown, nonlinear systems involving tens to thousands of input dimensions. Our solutions focus on the development, maintenance and deployment of transparent, robust, and interpretable input-response models, design- space exploration and exploitation, and model-based outlier detection. We discover the most elusive relationships in input-response data.

Dealing with Data Deluge

... Lots of variables. Little time. Lots of pressure... -- What variables really matter? What does it mean? Are there outliers? What to do with correlated inputs? How much do I know about my problem? What exactly don't I know about the problem? How to change it? Can I trust my conclusions? —These questions are raised in almost any data-driven industrial project.

Solving industrial projects by making sense of the data and turning data into value is our speciality.

Our technology will be interesting for

  • everyone who ever stared at a data spreadsheet;
  • everyone seeking an efficient, robust, and effective empirical modeling workflow;
  • everyone searching for a reliable variable selection methodology to reduce the dimensionality of the design space when correlated variables are present;
  • everyone hunting for outliers in the data, because they may be precious nuggets of information...