The modeling approach of ClearVu Analytics (CVA) provides you with the capability to automatically learn models from data. ClearVu Analytics applies a multitude of (nonlinear) modeling algorithms to the data, because it is not possible to know beforehand , which method will perform best.
For all modeling algorithms, ClearVu Analytics optimizes the corresponding learning parameters. This approach automatically guarantees application of the best algorithm and the ultimate generation of the best model for your data set. As an evaluation criterion for models their prediction accuracy is used. This approach completely avoids the often occurring phenomenon of overfitting of models.
After the automatic modeling process, a simple traffic light scheme guides you through the results and visualizes whether a good model was found and which modeling algorithm won the competition. You can directly proceed to use this model for prediction, sensitivity analysis, or optimization applications.
While ClearVu Analytics provides you with all modern methods of nonlinear data analysis, generating the optimal model is still very simple for the user. It just requires some computational effort. The results are presented to you in a comprehensible way. Among the available modeling algorithms are:
As an expert user, you can also set the learning parameters of the models by yourself – however, we recommend not doing so due to the power of the available automatic modeling technology.
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Thomas Bäck will introduce research results at the GECCO (Genetic and Evolutionary...
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