Content
Airfoil
The benchmark is found on Neural Designer, there is an internal link, but it may change one day. The dataset is Airfoil. It is experimental data with errors and uncertainties. The physical nature of the object is not important for accuracy test. Here is an example of several records: 10000;1.5;0.3048;39.6;0.00392107;108.991 12500;1.5;0.3048;39.6;0.00392107;106.111 400;3;0.3048;71.3;0.00425727;127.564 500;3;0.3048;71.3;0.00425727;128.454 630;3;0.3048;71.3;0.00425727;129.354First five values are features and the last one is target. Number of records is 1503. Our test is conducted using same training/validation concept and accuracy metrics. The dataset is divided into three parts, 60% is training set, 20% is selection set and 20% is testing set. Several models are created using training data, the accuracy is tested on selection data. The best model is considered selected and the final accuracy test is conducted on testing set. The accuracy metric is Pearson correlation coefficient. In our test it is the same as reported by Neural Designer, 95 to 96%. The execution time is about 1.5 sec. The code is not optimized for fast execution. |
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