
![:scale 55%](images/linear_boundary_vector.png)
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$$\hat{y} = \text{sign}(w^T \textbf{x} + b) = \text{sign}\left(\sum\limits_{i}w_ix_i + b\right)$$

???
Similar to the regression case, basically all linear models for classification have the same way
to make predictions. As with regression, they compute an inner product of a weight vector w with
the feature vector x, and add some bias b. The result of that is a real number, as in regression.
For classification, however, we only look at the sign of the res