The influence of the kernel width parameter to the performance of the RBF kernel SVM

Description

In this experiment RBF kernel SVMs are trained on the Pima Indians Diabetes data set with different kernel width parameter (gamma) values. The value of this parameter is increased from 0.001 to 5 while the value of the parameter C is fixed to 1 to obtain comparable results. The data set is split into a training and a test set, 75% of the examples are used to form a training set, and the rest are for testing. The classification error rates on both the training and the test sets are determined for each SVM.

Input

Pima Indians Diabetes [UCI MLR]

Output

Figure 8.21. The classification error rates of the SVM on the training and the test sets against the value of RBF kernel width parameter.

The classification error rates of the SVM on the training and the test sets against the value of RBF kernel width parameter.

Interpretation of the results

The value of the RBF kernel width parameter can be chosen such that the SVM will perfectly classify all training examples. Unfortunately, the model does not perform well on the test data. Apparently, overfitting occurs here. It should be noted that the linear SVM does not perform so well on the training set, its classification error rate is around 20%.

Video

Workflow

svm_exp7.rmp

Keywords

SVM
supervised learning
error rate
classification

Operators

Apply Model
Log
Loop Parameters
Normalize
Performance (Classification)
Read CSV
Split Data
Support Vector Machine (LibSVM)