I'm trying to create and test non-linear SVMs with various kernels (RBF, Sigmoid, Polynomial) in scikit-learn, to create a model which can classify anomalies and benign behaviors.
My dataset includes 692703 records and I use a 75/25% training/testing split. Also, I use various combinations of features whose dimensionality is between 1 and 14 features. However, the training processes of the various SVMs take much too long. Is this reasonable?
I have also examined the ensemble
BaggingClassifier in combination with non-linear SVMs, by configuring the
n_jobs parameter to
-1; nevertheless, the training process proceeds again too slowly.
How can I speed up the training processes?