ssociated with them.
It's not only the direction of the normal vector that matters, but also the length.
You can think of that as some form of uncertainty attached to the line.

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# Multinomial Logistic Regression

Probabilistic multi-class model:

$$p(y=i|x) = \frac{e^{\textbf{w}_i^T\textbf{x}}}{\sum_j e^{\textbf{w}_j^T\textbf{x}}}$$

`$$\min_{w \in ℝ^{p}} \sum_{i=1}^n \log(p(y=y_i|x_i))$$`

$$\hat{y} = \text{arg} \max_{i \in Y} \textbf{w}_i\textbf{x}$$

- Same prediction rule as OvR !

???
The binar