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.

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.

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.

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

"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.

"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.

"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

"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.

"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

Monday, March 24, 2014

We haven't been posting many updates lately - busy, busy, busy designing new cool products. This year an awesome 100% GUI-based interactive data analysis tool and at least one MatLab toolbox on information content estimation will be out. The pipeline for 2015-2016 is full and we are happy.

In February we have opened a second office in Belgium at Antoine Coppenslaan 27/11 in Turnhout!

Only whiteboards, some artwork, a few cables ...

Wednesday, March 12, 2014

We have a very nice update for our users!

This release features performance and option tuning to better exploit the OptimizeLinearModel capability introduced in the previous release as well as a number of enhancements to improve the results display and ease-of-use.

Friday, March 1, 2013

The highlights of this release are the new DataCompletenessMap and DataCompletenessPlot. These are useful to get the zen of data sets which feature incomplete data. Related to this, we have introduced a new SymbolicRegression option, NumericPredictionRequirement, since the default of evaluating model quality only on complete records in the data meant that models could, in extreme circumstances, be assessed on far fewer data records than expected even though each of the constituent variables...

Monday, December 3, 2012

We are glad to announce that this release should be compatible with both Mathematica 8 and 9!

  • The help has been rebuilt using Mathematica 9 so it is searchable using both versions.

Tuesday, November 27, 2012

We are happy to release DataModeler 8.12. There are several important highlights to mention:

  • Archived models are now automatically compressed to reduce file sizes (factors of 25 are a good thing).
  • Implemented support for VariablesToPlot option in a variety of functions — this makes data and performance exploration much cleaner for high-dimensional data sets as we gain insight into the key inputs.

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...