# Introduction

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.

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