# More than two classes: Logistic Regression In the following, we are interested in the case of $p$ classes with $p>2$. After the previous discussion, it seems natural for the output to take the integer values $y = 1, \dots, p$. However, it turns out to be helpful to use a different, so-called *one-hot encoding*. In this encoding, the output $y$ is instead represented by the $p$-dimensional unit vector in $y$ direction $\mathbf{e}^{(y)}$, {math} :label: eqn:One-Hot-Encoding y \longrightarrow \mathbf{e}^{(y)} = \begin{bmatrix} e^{(y)}_1 \\ \vdots \\ e^{(y)}_y \\ \vdots \\ e^{(y)}_{p} \end{bmatrix} = \begin{bmatrix} 0 \\ \vdots \\ 1 \\ \vdots \\ 0 \end{bmatrix},  where $e^{(y)}_l = 1$ if $l = y$ and zero for all other $l=1,\ldots, p$. A main advantage of this encoding is that we are not forced to choose a potentially biasing ordering of the classes as we would when arranging them along the ray of integers. A linear approach to this problem then again mirrors the case for linear regression. We fit a multi-variate linear model, Eq. [](eqn:Multivariate-Linear-Model), to the one-hot encoded dataset $\lbrace(\mathbf{x}_{1}, \mathbf{e}^{(y_1)}), \dots, (\mathbf{x}_{m}, \mathbf{e}^{(y_m)})\rbrace$. By minimising the RSS, Eq. [](eqn:RSS), we obtain the solution {math} \hat{\beta} = (\widetilde{X}^{T}\widetilde{X})^{-1} \widetilde{X}^{T} Y,  where $Y$ is the $m$ by $p$ output matrix. The prediction given an input $\mathbf{x}$ is then a $p$-dimensional vector $\mathbf{f}(\mathbf{x}|\hat{\beta}) = \tilde{\mathbf{x}}^{T} \hat{\beta}$. On a generic input $\mathbf{x}$, it is obvious that the components of this prediction vector would be real valued, rather than being one of the one-hot basis vectors. To obtain a class prediction $F(\mathbf{x}|\hat{\beta}) = 1, \dots, p$, we simply take the index of the largest component of that vector, i.e., {math} F(\mathbf{x}|\hat{\beta}) = \textrm{argmax}_{k} f_{k}(\mathbf{x}|\hat{\beta}).  The $\textrm{argmax}$ function is a non-linear function and is a first example of what is referred to as *activation function*. For numerical minimization, it is better to use a smooth activation function. Such an activation function is given by the *softmax* function {math} F_k(\mathbf{x}|\hat{\beta})= \frac{e^{-f_k(\mathbf{x}|\hat{\beta})}}{\sum_{k'=1}^pe^{-f_{k'}(\mathbf{x}|\hat{\beta})}}.  Importantly, the output of the softmax function is a probability $P(y = k|\mathbf{x})$, since $\sum_k F_k(\mathbf{x}|\hat{\beta}) = 1$. This extended linear model is referred to as *logistic regression* [^3]. The current linear approach based on classification of one-hot encoded data generally works poorly when there are more than two classes. We will see in the next chapter that relatively straightforward non-linear extensions of this approach can lead to much better results. [^3]: Note that the softmax function for two classes is the logistic function.