Table of Contents
In this experiment, we teach a perceptron as a simple artificial neural network (ANN) and a support vector machine ( SVM) on a linearly separable two dimensional dataset with two classes. The dataset is a subset of the Wine dataset. adatállomány egy részhalmaza. The classification accuracy of the classifiers is determined on the dataset.
Wine [UCI MLR]
In order to apply the dataset for the experiment it have to go through a significant preprocessing.
This involves choosing
2 attributes from the total
13 by using the
Drop operator, then deleting the second class of the
and changing the measure level to binary by
The goodness of fit of the perceptron model can be checked by the usual statistics (misclassification rate, the number of incorrectly classified cases) and graphics (response and lift curve).
In the case of support vector machines (SVM), besides the above mentioned diagnostic tools, more details are given about the support vectors, namely the number of support vectors, the size of the margin and the list of support vectors.
The figures and statistics show that the perceptron and support vector machine classify perfectly all of the traning cases as well.