How the SVM algorithm works with one label in range? I have a dataset where the features are configurations (numeric values) that describe the situation and the label (only one) is the ranking of the situation (natural value between $[1,5]$). If label is $1$ then the configurations are bad and if the label is $5$ then the configurations are great.
I'm trying to use the SVM algorithm. As I understand, the SVM algorithm is designed for binary classifiers and my problem requires a multi-class classifier. I'm trying to understand how the one-vs-one and one-vs-rest can help here. Is it possible to explain how the SVM algorithm works with those two approaches in the case of one label in range [1,5]? Does my dataset considered as multiclass dataset? What If I have five labels (ranking $[1,5]$) and each one has a binary output ($0$ or $1$)? I'm a bit confused about it.


Is it possible to explain how the SVM algorithm works with those two
approaches in the case of one label in range [1,5]

Your target variable (the 'label') $y$ can have 5 realizations: 1,2,3,4,5. Classifying observations into these 5 categories is considered a multiclass problem, yes.
The two approaches that you mentioned, one-vs-one and one-vs-rest, aim to make a binary classifier applicable to non-binary classification problems. Note that these approaches are not limited to SVM. So what do these approaches do?
One-vs-one
In your case, $|y| \cdot \frac{|y| - 1}{2}=10$ models are trained, where |y| is 5 in your case. Each model discriminates between different realizations of $y$:

*

*1 or 2

*1 or 3

*1 or 4

*1 or 5

*2 or 3

*...

*4 or 5

One-vs-rest
A coarser approach that fits 5 models that perform a 'this or other' discrimination:

*

*1 or not 1

*2 or not 2

*3 or not 3

*4 or not 4

*5 or not 5

It is noticable, that the number of models that need to be fitted is growing quite quickly for the one-vs-one approach, while one-vs-rest always requires |y| models.

What If I have five labels (ranking [1,5]) and each one has a binary output (0 or 1)? I'm a bit confused about it.

Say instead of the vector $y$ that we had above, you have a matrix $Y$ with one column per possible class realization. The values in each column are binary, 0 or 1.
Assuming that the row sums are 1 for every row, meaning that an observation is only assigned to one class, then we basically end up with the same method as with one-vs-rest. For each column in $Y$, one can fit a model that distinguishes between the respective class and 'other'.
