ining set. Where classification differs quite a bit from
regression is on how we want to measure misclassifications.


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# Picking a loss?

$$\hat{y} = \text{sign}(w^T \textbf{x} + b)$$

`$$\min_{w \in ℝ^{p}} \sum_{i=1}^n 1_{y_i \neq \text{sign}(w^T \textbf{x} + b)}$$`

.center[
![:scale 40%](images/binary_loss.png)
]

???
So we need to define a loss function for given w and b that tell us how well they fit the training set.
Obvious Idea: Minimize number of misclassifications aka 0-1 loss
but this loss