Others (like error rate) are difficult to optimize directly, owing to non-differentiability or other complications. In these cases, it is common to optimize a surrogate objective

It’s not quite clear to me from reading what exactly is meant with “error rate”. I think it would be great if an example could be given.

Hi @manuel-arno-korfmann, “error rate” means “how much mistake the model makes”. Is that more clear?

I am still unable to understand error rate.
“How much mistake to model makes” is not clear enough, did you mean how much mistake ‘the’ model makes, which is L1 distance(y-y¹).
Also can you please explain what is surrogate objective?

I’m having a difficult time understanding

Hence, the loss 𝐿L incurred by eating the mushroom is 𝐿(𝑎=eat|𝑥)=0.2∗∞+0.8∗0=∞L(a=eat|x)=0.2∗∞+0.8∗0=∞, whereas the cost of discarding it is 𝐿(𝑎=discard|𝑥)=0.2∗0+0.8∗1=0.8L(a=discard|x)=0.2∗0+0.8∗1=0.8.

Is it possible to explain it in more depth via 1 or 2 paragraphs?

Ok, so a person in the reading group explained that the error rate is the accumulated loss for all examples, is that correct?

Hey @syedmech47, Sorry for the typo here. Yes you got the idea here - the error rate is to measure the distance between y (the truth) and the $\hat{y}$ (the estimate). However the measurement metrics (which measure the error) does not limit to L1 distance, but also can accuracy, precision, recall, f1, etc.

A surrogate is a function that approximates an objective function. There are lots of measurement metrics are not differentiable (like f1 etc.), hence we need some other functions (i.e., the loss function ) to approximate the objective function.

Let me know if this is clear enough!

It can be the accumulated loss, or average loss. It doesn’t make a lot difference here for optimization.

Thanks a lot. It totally made sense.

Side Note: I just want to thank each and every person’s effort in making this wonderful resource open for all and also providing such wonderful support through discussion forums.

Fantastic! It’s our pleasure to enable more talents learn, apply and benefit from deep learning!