How do you plot the hyperplane of an sklearn svm with more than 2 features in matplotlib?
If your linear SVM classifier works quite well, then that suggests there is a hyperplane which separates your data.  So there will be a nice 2D geometric representation of the decision boundary.  
To understand the "how" you need to look at the support vectors themselves, see which ones contribute to which side of the hyperplane, e.g., by feeding individual support vectors into the trained classifier.  In general, visualising text algos is not straightforward.
I have a scikits-learn linear svm.SVC classifier designed to classify text into 2 classes (-1,1). The classifier uses 250 features from the training set to make its predictions, and it works fairly well. 
However, I can't figure out how plot the hyperplane or the support vectors in matplotlib. All the examples online use only 2 features to derive the decision boundary and the support vector points. I can't seem to find any that plot hyperplanes or support vectors that have more than 2 features or lack fixed features. I know that there is a fundamental mathematical step that I am missing here, and any help would be appreciated.