Machine Learning with R Cookbook - Second Edition

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Besides using SVM to predict the category of new observations, you can use SVM to predict continuous values. In other words, one can use SVM to perform regression analysis.

In the following example, we will show how to perform a simple regression prediction based on a fitted SVM with the type specified as eps-regression.

Perform the following steps to train a regression model with SVM:

1. Train a support vector machine based on a Quartet dataset:

        > library(car)
        > data(Quartet)
        > model.regression = svm(Quartet$y1~Quartet$x,type="eps        -regression")

2. Use the predict function to obtain prediction results:

        > predict.y = predict(model.regression, Quartet$x)         > predict.y        Output           1       2         3        4         5       8.196894 7.152946 8.807471 7.713099 8.533578 ...