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? enter image description here

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

  • $\begingroup$ Can you please point to the source of these formulas? $\endgroup$ – usεr11852 Feb 7 at 2:40

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