Visualizing an SVM fit

To visualize the built model, one can first use the plot function to generate a scatter plot of data input and the SVM fit. In this plot, support vectors and classes are highlighted through the color symbol. In addition to this, one can draw a contour filled plot of the class regions to easily identify misclassified samples from the plot.

Getting ready

In this recipe, we will use two datasets: the iris dataset and the telecom churn dataset. For the telecom churn dataset, one needs to have completed the previous recipe by training a support vector machine with SVM and to have saved the SVM fit model.

How to do it...

Perform the following steps to visualize the SVM fit object:

1. Use SVM to train the support vector machine based on the iris dataset and use the plot function to visualize the fitted model:

        > data(iris)
        > model.iris  = svm(Species~., iris)
        > plot(model.iris, iris, Petal.Width ~ Petal.Length, slice =
        list(Sepal.Width
    ...