I am trying to use scikit-learn for building decision trees representing fully defined many-to-one functions. i.e. f(x1, x2, ..., xn) = f(x1', x2', ..., xn') if and only if x1 = x1', x2 = x2' and so on. In such a scenario, it seems clear that it is possible to construct trees which have pure leaves as long as min_samples_split = 2 and I am not setting any other parameters which might stop the splitting. My question is whether the decision tree builder in scikit-learn can indeed give me a perfect representation, i.e where all leaf nodes are pure. This would imply that tree.predict(x) = f(x).
Thanks and best regards.
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