Extract decision boundary with scikit-learn linear SVM

Question

I have a very simple 1D classification problem: a list of values [0, 0.5, 2] and their associated classes [0, 1, 2]. I would like to get the classification boundaries between th

Answer 1

I had the same question and eventually found the solution in the sklearn documentation.

Given the weights W=svc.coef_[0] and the intercept I=svc.intercept_ , the decision boundary is the line

y = a*x - b

with

a = -W[0]/W[1]
b = I[0]/W[1]