When would you use random forests Vs SVM and why?

Data Science Interview QuestionsCategory: Data ScienceWhen would you use random forests Vs SVM and why?
1 Answers
MockInterview Staff answered 5 years ago
  • In a case of a multi-class classification problem: SVM will require one-against-all method (memory intensive)
  • If one needs to know the variable importance (random forests can perform it as well)
  • If one needs to get a model fast (SVM is long to tune, need to choose the appropriate kernel and its parameters, for instance sigma and epsilon)
  • In a semi-supervised learning context (random forest and dissimilarity measure): SVM can work only in a supervised learning model

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