The influence of the parameter C to the performance of the linear SVM (2)

Description

The process demonstrates the influence of the parameter C on the average classification error rate of the linear SVM in the case of the Heart Disease data set. We consider linear SVMs with different C values, each of which is an integer power of 2: C = 2^n, where -13 <= n <= 6. The average classification error rate from 10-fold cross-validation is determined for each linear SVM.

Input

Heart Disease [UCI MLR]

Note

The data set was donated to the UCI Machine Learning Repository by R. Detrano [Detrano et al.].

Output

Figure 8.12. The average classification error rate of the linear SVM obtained from 10-fold cross-validation against the value of the parameter C, where the horizontal axis is scaled logarithmically.

The average classification error rate of the linear SVM obtained from 10-fold cross-validation against the value of the parameter C, where the horizontal axis is scaled logarithmically.

Interpretation of the results

The figure shows that the average classification error rate is minimal when the value of the parameter C is 2^-8. Larger values of C result in a slightly worse average classification performance. However, values closer to zero give worse result.

Thus, the performance of the linear SVM seems not to be sensitive to the value of the parameter C in this case.

Video

Workflow

svm_exp3.rmp

Keywords

SVM
supervised learning
error rate
classification
cross-validation

Operators

Apply Model
Filter Examples
Log
Loop Parameters
Map
Normalize
Performance (Classification)
Read CSV
Support Vector Machine (LibSVM)
X-Validation