Thread:Talk:BeepBoop/Awesome enty/reply (16)

I tend to think it's right that the KNN would take such relationships of features into account in a sense, but as a statistical model what it cannot do is generalize, which increases the number of data points needed to effectively cover some areas of the input space. In many ways, for this sort of usage, I would conceptualize the potential advantage of a deep embedding not as learning the feature interactions themselves, so much as learning the generalized contour of when to de-weight features, as a noise filter of sorts. This is a bit of a tangent, but thinking of in in terms of being like a noise filter, and also considering things like BeepBoop's velocity randomization, I also start to wonder if there could be some value in including not just the present feature values as inputs to deep embeddings, but including several ticks worth of feature history.