# Test for linear separability

Is there a way to test linear separability of a two-class dataset in high dimensions? My feature vectors are 40-long.

I know I can always run logistic regression experiments and determine hitrate vs false alarm rate to conclude whether the two classes are linearly separable or not but it would be good to know if there already exists a standard procedure to do that.

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have a look here: – user603 Jan 17 at 10:11
It's useful to plot separabiity: x = misclassified points $\cdot$ normal-to-separating-plane, y = cumulative loss(x). (For a sample plot, try a new question with tags svm and data-visualization.) – Denis Feb 3 at 11:13

+1. It's almost as if Nik were describing SVM's, not having heard of them. In R, you could use the (mysteriously-named) e1071 package's svm with kernel="linear" and look at the prediction versus actual. – Wayne Jan 19 at 15:13