• 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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