Learning-Agnostic Evaluation of SVM Models I am at a point where I want to evaluate existing SVM models.
For this task I assume I have:

*

*SVM model (to make it easier let's say it's a scikit-learn one)

*Training Dataset that was used to learn (1)

What measures can I possibly compute to evaluate the SVM model?
The way I see it, my possibilities are limited:

*

*I could compute accuracy etc. on the given dataset (2).
This is of course not really what anyone wants, because the data for testing should be different from the data for training

*I could train my own SVM models and compare them to (1) in some way.

And that's about the end of what I can think of.
I would love to have any other ways to evaluate the given model in whatever metric (Accuracy, Robustness, Compliance with some proven bound, ...).
Are there any more possible ways to evaluate? If more information about the training-process of the model is needed, what information would be helpful?


What measures can I possibly compute to evaluate the SVM model?

It depends on the given task. If you build a predictive model, then evaluation with accuracy or other metrics makes sense (see, for example, Classification Metrics). Otherwise, you are probably interested in making inference about the model. Then try exploring the support vectors, misclassified examples, and decision surface of your trained model (see SVM Guide).

I could compute accuracy etc. on the given dataset (2). This is of course not really what anyone wants, because the data for testing should be different from the data for training

Again, if you wish your predictive model to generalize to other data, then try cross-validation along with the chosen set of evaluation metrics.

I could train my own SVM models and compare them to (1) in some way.

I don't think it's necessary. Speaking of scikit-learn SVMs, they are pretty much state-of-the-art.
