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In the context of the implementation of tree based models, suppose that a predictor space $X \in \R^n$ is under a partition process of the algorithm recursive binary splitting.
So the recursive binary splitting compute a metric across those predictors, in the case of continuous variables used the residual sum of the squares and when the values are predefined and can identify under a label, categorical variables, used cross-entropy or gini index.
How the implementations that use the mechanism describe above can automatically identify which of the predictor variables, $X_1, ..., X_n$, are categorical and which ones are continuos? or just the identification stage of the algorithm, the stage to define which metric compute for the predictor $X_i$, it's based in the data type of each variable?
Yearsof a patient ${14, 28, 32, 12, ..}$, how the algorithm determine: "ahh! this is a continuos variable, use the RSS to compute the loss function based in the cut points and find the minimum"? or just the implementation relies on: "variableYearsis a float type variable, so I use RSS, or the case ofYearsis a integer type then I use gini or entropy measures". – Cristóbal Alcázar Aug 30 '17 at 13:38