The tree-classification algorithm can handle both discrete and continuous. There is no such thing "rule of thumb" when to transform and when not to transform, because it all depends on how you view your data. If you believe your data should be categorical (for example, income-groups), you should use a factor variable. However, if precision is required (eg: income per month), then you'll need to do it continuously.

Ask yourself, if you were a user, which way to view the data would make more sense? For example, when you complete tax-returns, you'd have to give your exact income. However, you'd need to talk your friend a rough-estimate (eg: 2000-3000 per month).

The tree-classification algorithm can handle both discrete and continuous. There is no such thing "rule of thumb" when to transform and when not to transform, because it all depends on how you view your data. If you believe your data should be categorical (for example, income-groups), you should use a factor variable. However, if precision is required (eg: income per month), then you'll need to do it continuously.

Ask yourself, if you were a user, which way to view the data would make more sense? For example, when you complete tax-returns, you'd have to give your exact income. However, you'd only need to tell your friend a rough-estimate (eg: 2000-3000 per month).