[KMoff]

Hello, I am having trouble understanding the procedure for binary classification using linear regression.

For ordinary linear regression, as I understand it, we may compute the weights by taking the product pseudo-inverse matrix and the y-vector. In 2D, the line obtained by linear regression is then y = w0 + w1 * x.

Now for the binary case, instead of using the actual y-coordinate value of a data point, we use its binary classification relative to the target function. In this case, the only difference would be that the y-vector which is to be multiplied by the pseudo-inverse matrix consists only of +1 and -1 values.

However, when I try this I get an hypothesis line nearly perpendicular to the target function.

Could someone please clarify? Thanks