Loss Function

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Tags: concept

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Summary:

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The Loss Function quantifies how bad $\theta$ is.

We want the least bad $\theta$ .

Examples

$-\sum_{i} \log p_{\theta}\left(y_{i} \mid x_{i}\right)$

-\sum_{i} \log p_{\theta}\left(y_{i} \mid x_{i}\right)

$-\sum_{i} \delta\left(f_{\theta}\left(x_{i}\right)=y_{i}\right)$

$\sum_{i} \frac{1}{2}\left|f_{\theta}\left(x_{i}\right)-y_{i}\right|^{2}$

The Loss Function quantifies how bad $\theta$ is.

Loss Function measures if one model in the model class is better than another.