# Loss Functions

In this post, I will summarize some common loss functions in machine learning. Some of the definitions come from Professor Liang in the lecture note of CS221 at Stanford.

A cheatsheet of loss functions can be found here.

According to Professor Liang, a loss function Loss\((x, y, \mathbf w)\) quantifies how unhappy you would be if you used \(\mathbf w\) to make a prediction on \(x\) when the correct output is \(y\). It is the object we want to minimize.

One thing we need to keep in mind is that loss function is an individual topic independent of prediction models.

## Zero-one loss

\[\text{Loss}_{0-1}(x, y, \mathbf w) = 1[f_{\mathbf w}(x) \ne y]\]## Squared loss (L2 loss)

First we define residual. The residual is \((\mathbf w · \phi (x)) − y\), the amount by which prediction \(f_{\mathbf w}(x) = \mathbf w · \phi (x)\) overshoots the target \(y\). Then we have

\[\text{Loss}_{\text{squared}}(x, y, \mathbf w) = (f_{\mathbf w}(x) - y)^2\]Squared loss is used in Linear Regression.

## Absolute deviation loss (L1 loss)

\[\text{Loss}_{\text{absdev}}(x, y, \mathbf w) = |f_{\mathbf w}(x) - y|\]## Cross entropy loss

\[\text{Loss}_{\text{cross-entropy}}(x, y, \mathbf w) = y\log f_{\mathbf w}(x) +(1-y) \log (1-f_{\mathbf w}(x))\]Cross entropy is used in Logistic Regression.

## Hinge loss

\(\text{Loss}_{\text{hinge}}(x,y,\mathbf w) = \begin{cases} max (0, 1-f_{\mathbf w}(x)) & \text{if } y=1\\ max (0, 1+f_{\mathbf w}(x)) & \text{if } y=0\end{cases}\)

Hinge loss is used in SVM.