For svm with linear kernel, the boundary is given by the hyperplane at which the score function is equal to zero. In case of a single predictor, the "hyperplane" is a point. And the score function is 
where β is the coefficient, and b is bias. Hence, you can get the boundary for solving the equation for 
. Refer the code below for example.
where β is the coefficient, and b is bias. Hence, you can get the boundary for solving the equation for . Refer the code below for example.X = [1:100]';
y = X < 80;
mdl = fitcsvm(X,y, 'KernelFunction',"linear");
% the equation for decision boundary is beta * x + bias = 0
% so x = -bias/beta
bnd = -1 * mdl.Bias / mdl.Beta
Note that this works only for linear kernels. For non linear kernels, SVM does not calculate score in terms of parameters, hence it's not possible to get an equation for the boundary. See this answers for details. You should use linear classifier in such a situation.
