I have a question about support vector machines. I was watching Andrew Ng's lecture on "Large Margin Intuition". Starting from 0:18 of the video, he says the following:

If $ y = 1$, we want $\theta^Tx \geq 1$ (not just $\geq 0$)

If $ y = 0$, we want $\theta^Tx \leq -1$ (not just $< 0$)

Please see screen shot of video below: enter image description here

My question is the following: What if we have an observation in which $-1<\theta^Tx<1$? How do we classify the observation in that case? Is it $y = 0$? Or is it $y = 1$? Or is it neither?


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