Support Vector Machine with GPU, Part II

In our last tutorial on SVM training with GPU, we mentioned a necessary step to pre-scale the data with rpusvm-scale, and to reverse scaling the prediction outcome. This cumbersome procedure is now simplified with the latest RPUSVM.

For example, we can work directly with the cadata from the LIBSVM site. Just load it into the R workspace with read.svm.data and apply the function rpusvm right away. The overhead of the implicit data scaling turns out to be rather negligible.

We can inspect the range of each attribute of cadata in the SVM model cdata.rpusvm. In particular, for a data set with N attributes, the x.bound component of cdata.rpusvm is a 2 × N matrix, with each column containing the lower and upper bounds of the corresponding attribute. Likewise, the y.bound component contains the lower and upper bounds of the response variable.

Using the residuals component of the SVM model, we can compute the mean square error:

As for prediction, we can apply the function predict on a test data set without further post-processing:

If we decide to train an SVM in a terminal, we can apply the standalone rpusvm tool by adding extra scale parameters:

A glance of the SVM model cadata.rpusvm shows that the scale parameters are gathered in the header section.

Finally, we can use the model for prediction without pre-scaling the test data or post-scaling the outcome.