The influence of the parameter C to the performance of the linear SVM (1)

Description

The process demonstrates the influence of the parameter C on performance of the linear SVM. Linear SVMs are trained on a subset of the Wine data set while the value of the parameter C is increased from 0.001 to 100. The classification error rate on the training set and also the number of support vectors are determined for each SVM.

Input

A subset of the Wine data set [UCI MLR].

Figure 8.9. A subset of the Wine data set used in the experiment (2 of the total of 3 classes and 2 of the total of 13 attributes was selected). Note that the classes are not linearly separable.

A subset of the Wine data set used in the experiment (2 of the total of 3 classes and 2 of the total of 13 attributes was selected). Note that the classes are not linearly separable.

Output

Figure 8.10. The classification error rate of the linear SVM against the value of the parameter C.

The classification error rate of the linear SVM against the value of the parameter C.

Figure 8.11. The number of support vectors against the value of the parameter C.

The number of support vectors against the value of the parameter C.

Interpretation of the results

The first figure shows that the classification error rate quickly falls below 6% as the value of the parameter C is increased and then it remains constant.

The second figure shows that the number of support vectors decreases similarly with the increasing value of the parameter C, although not so rapidly as the classification error rate.

Video

Workflow

svm_exp2.rmp

Keywords

SVM
supervised learning
error rate
classification

Operators

Apply Model
Filter Examples
Log
Loop Parameters
Normalize
Performance (Classification)
Performance (Support Vector Count)
Read CSV
Remove Unused Values
Support Vector Machine (LibSVM)