Operators Apply Model Normalize Performance (Classification) Read CSV Support Vector Machine (LibSVM) 13. Using an SVM for solving a regression problem Description The process demonstrates how to use an SVM for solving a regression problem. In this experiment RBF kernel SVMs are trained on the data set while the value of the parameter of the Concrete Compressive Strength gamma RBF kernel is changed. To obtain comparable results the value of the parameter is fixed to 10. The average C RMS error from 10-fold cross-validation is determined for each SVM. As a result, the value yielding the gamma best average RMS error will be returned. Using this value for the parameter an RBF kernel SVM is gamma trained on the entire data set that is referred to as the RBF kernel SVM” below. “optimal Input [] [] Concrete Compressive Strength Output Figure 8.26. The optimal of the parameter for the RBF kernel SVM. gamma Figure 8.27. The average RMS error of the RBF kernel SVM obtained from 10-fold cross-validation against the value of the parameter where the horizontal axis is gamma, scaled logarithmically. Figure 8.28. The kernel of the optimal RBF kernel SVM. Figure 8.29. Predictions provided by the optimal RBF kernel SVM against the values of the observed values of the dependent variable. Interpretation of the results The first figure shows that the best average RMS error is achieved when the value of the parameter is gamma = 0.25. The third figure shows that the average RMS error decreases with the increasing value of the parameter gamma until it reaches its minimum. However, further increase of the value of the parameter results in the gamma degradation of the performance, i.e., model overfitting occurs. Video
Operators Apply Model Normalize Performance (Classification) Read CSV Support Vector Machine (LibSVM) 13. Using an SVM for solving a regression problem Description The process demonstrates how to use an SVM for solving a regression problem. In this experiment RBF kernel SVMs are trained on the Concrete Compressive Strength data set while the value of the parameter gamma of the RBF kernel is changed. To obtain comparable results the value of the parameter C is fixed to 10. The average RMS error from 10-fold cross-validation is determined for each SVM. As a result, the gamma value yielding the best average RMS error will be returned. Using this value for the parameter gamma an RBF kernel SVM is trained on the entire data set that is referred to as the “optimal RBF kernel SVM” below. Input Concrete Compressive Strength [] [] Output Figure 8.26. The optimal of the gamma parameter for the RBF kernel SVM. Figure 8.27. The average RMS error of the RBF kernel SVM obtained from 10-fold cross-validation against the value of the parameter gamma, where the horizontal axis is scaled logarithmically. Figure 8.28. The kernel of the optimal RBF kernel SVM. Figure 8.29. Predictions provided by the optimal RBF kernel SVM against the values of the observed values of the dependent variable. Interpretation of the results The first figure shows that the best average RMS error is achieved when the value of the parameter gamma is = 0.25. The third figure shows that the average RMS error decreases with the increasing value of the parameter gamma until it reaches its minimum. However, further increase of the value of the parameter gamma results in the degradation of the performance, i.e., model overfitting occurs. Video
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