# Why is it useful to track loss while model is being trained?

Why is it useful to track loss while the model is being trained?

Options are:

1. Loss is only useful as a final metric. It should not be evaluated while the model is being trained.
2. Loss dictates how effective the model is.
3. Loss can help understand how much the model is changing per iteration. When it converges, that's an indicator that further training will have little benefit.
4. None of the above
• Hi and welcome to AI SE! Do you know something about machine learning or neural networks? This question should be easy to answer if you know something about neural networks and you have trained at least one once in your life. – nbro Jun 14 at 15:05
• This looks like coursework? If it is, could you please add some of your own thoughts/analysis (use edit). Also, you may want to consider whether you have agreed to any code of conduct on such a course - e.g. if your correct answer leads to gaining a certificate - because posting a question from course material directly in order to get someone else to answer for you may lead to your results being nullified. – Neil Slater Jun 14 at 20:39

For example, a simple cost function could be $$|y - f(x)|$$, where
• $$\hat{y} = f(x)$$ is the prediction of the model $$f$$ when the input is $$x$$,
• $$y$$ is the ground-truth label for input $$x$$ (i.e. what the model is supposed to output when the input is $$x$$), and
• $$|\cdot|$$ is the absolute value