Slice = list(Sepal.Width=3, Sepal.Length=4)) If we try to use SVM with a linear kernel (not shown here), instead of a SVM with a radial kernel, the missclassification rate is a bit higher. We have also a missclassification rate, of 2.6%. Similarly, we have points depicted with red cross points for versicolor, and green cross points for virginica.įrom the Confusion Matrix above, we have only 2 observation missclassified for versicolor, and 2 observation missclassified for virginica. Inside the blue class setosa we have 8 points depicted with a cross, and these are the suppor vectors for setosa. The Species classes are shown in different shadings. The two-dimensional plot above, is a projection of the data with highlighting classes and support vectors. Virginica 0 2 48 # Missclassification RateĪs we can see from the result above, we use Gaussian Radial Basis Function, cost is the constaint violation.

Tab <- table(Predicted = pred, Actual = iris$Species) # Confusion Matrix and Missclassification Error Slice = list(Sepal.Width=3, Sepal.Length=4)) # specify a list of named values for the dimensions held constant # The Species classes are shown in different shadings Setosa versicolor virginica # Plot two-dimensional projection of the data with highlighting classes and support vectors In this case, we use the the Kernel Trick, using the Gaussian Radial Basis Function. In situatins where we are not able to obtain a linear separator, the data are projected into a higher dimentional space, so that, data points, can become linearly separable. The point that lie on the boundary ar called Support Vectors, and the middle line is the Seprarating Hyperplane. And to do that SMV maximize the margin around the hyperplane. With Support Vector Machine SVM we are looking for optimal separating hyperplane between two classes. From the graph above, we can see there is a separation based on the Species, for example setosa species is very far from the other two groups, and between versicolor and virginica there is a small overlap. We create classification machine learning model that help us to predict the correct species. We can also see that the numerical variables have different ranges, it is a good pratice to normalize the data. We are using the iris dataset with 4 numerical variables and 1 factor which has 3 levels as described above. # qplot(Petal.Length, Petal.Width, data=iris, color = Species) Head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width SpeciesĦ 5.4 3.9 1.7 0.4 setosa # library(ggplot2) Summary(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width With Margins we mean the distance between the hyperplane and the closest elements from the hyperplane. We can have many possible hyperplanes that are able to classify correctly all the elements in the feature set, but the best choice will be the hyperplane that leaves the Maximum Margin from both classes.

The hyperplane classifies all the training vectors in two classes. If a space is 3-dimensional then its hyperplanes are the 2-dimensional planes). The goal is to design a hyperplane (is a subspace whose dimension is one less than that of its ambient space. We can consider SVM for linearly separable binary sets. Support Vector Machine SVM is a linear classifier.

Plot hyperplan