python - SKLearn: Getting distance of each point from decision boundary?

Question

I am using SKLearn to run SVC on my data.

from sklearn import svm

svc = svm.SVC(kernel='linear', C=C).fit(X, y)

I want to know how I can get the distance of each data point in X from the decision boundary?

Answer 1

For linear kernel, the decision boundary is y = w * x + b, the distance from point x to the decision boundary is y/||w||.

y = svc.decision_function(x)
w_norm = np.linalg.norm(svc.coef_)
dist = y / w_norm

For non-linear kernels, there is no way to get the absolute distance. But you can still use the result of decision_funcion as relative distance.