In part II of this series it has been discussed and demonstrated -with numbers-, that features fit best a variable when they are assumed to be in a curved space, probably a positively curved one. This novel perspective was selected due to the fact that stochastic processes are seemingly random, but as everything is governed by numbers, there must be a way to well define it.
The Hyperspace is not Always Euclidean: f1 Score in Positive Curvature Fits Best Data - Part IV
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