Some predictive modeling techniques are more designed for handling continuous predictors, while others are better for handling categorical or discrete variables. Of course there exist techniques to transform one type to another (discretization, dummy variables, etc.). However, are there any predictive modeling techniques that designed to handle both types of input at the same time without simply transforming the type of the features? If so, do these modeling techniques tend to work better on data for which they are a more natural fit?
The closest thing that I know of would be that usually decision trees handle discrete data well and they handle continuous data without requiring an up front discretization. However, this isn't quite what I was looking for since effectively the splits on continuous features are just a form of dynamic discretization.
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