Svm vs random forest - Explain the difference.
When would one use Random Forest over SVM and vice versa? I understand that cross-validation and model comparison is an important aspect of choosing a model, but here I would like to learn more about rules of thumb and heuristics of the two methods.
Can someone please explain the subtleties, strengths, and weaknesses of the classifiers as well as problems, which are best suited to each of them?
svm vs random forest SVM models perform better on sparse data than trees in general. For example in document classification you may have thousands, even tens of thousands of features and in any given document vector only a small fraction of these features may have a value greater than zero. There are probably other differences between them, but this is what I found for my problems.