I'm working on a classification problem (predicting three classes) and I'm comparing SVM against Random Forest in R.
For evaluation and comparison I want to calculate the bias and variance of the models. I've looked up the two terms in many machine learning books and I'd say I do understand the sense of variance and bias (easiest explanation with the bullseye). But I can't really figure out how to apply it in my case.
Let's say I predict the results for a test set with 4 SVM-models that were trained with 4 different training sets. Each time I get a total error (meaning all wrong predictions/all predictions). Do I then get the bias for SVM by calculating this?
which would mean that the bias is more or less the mean of the errors?
I hope you can help me with not to complicated formula, because I've already seen many of them :D
Tanks in advance