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Linear Regression Classification HW2 Q5/6
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 pseudoinverse matrix and the yvector. In 2D, the line obtained by linear regression is then y = w0 + w1 * x. Now for the binary case, instead of using the actual ycoordinate 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 yvector which is to be multiplied by the pseudoinverse 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 
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