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`

`gamma`

`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
**

`gamma`

, where the horizontal axis is
scaled logarithmically.**Figure 8.29. Predictions provided by the optimal RBF kernel SVM against
the values of the observed values of the dependent variable.**

The first figure shows that the best average RMS error is achieved
when the value of the parameter * gamma* is
2^-2 = 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.