Using a linear SVM for solving a linearly separable binary classification problem

Description

In this experiment a linear SVM is trained on a linearly separable two-dimensional data set consisting of two classes, that is a subset of the Wine data set. The classification accuracy of the linear SVM is determined on the data set.

Input

Figure 8.6. A linearly separable subset of the Wine data set [UCI MLR] used in the experiment (2 of the total of 3 classes and 2 of the total of 13 attributes was selected).

A linearly separable subset of the Wine data set [UCI MLR] used in the experiment (2 of the total of 3 classes and 2 of the total of 13 attributes was selected).

Output

Figure 8.7. The kernel model of the linear SVM.

The kernel model of the linear SVM.

Figure 8.8. The classification accuracy of the linear SVM on the data set.

The classification accuracy of the linear SVM on the data set.

Interpretation of the results

The figure shows that the linear SVM perfectly classifies all training examples.

Video

Workflow

svm_exp1.rmp

Keywords

SVM
supervised learning
classification

Operators

Apply Model
Filter Examples
Performance (Classification)
Read CSV
Remove Unused Values
Support Vector Machine (LibSVM)