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Explainable K-Means

Data Skeptic

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Having a Shorter Tree Depth

Basi: We try to come up with a penalty for cuts that separate data in an unballanced way. The quality of the cut, i can do by figuring out how many points get separated from their original centroids. If we don't care about depth, and if we introduce a small penalty, i believe it was three %, then it comes up with trees that are much shallower on average.

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