[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors

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  • CloudForest

    Ensembles of decision trees in go/golang.

  • The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest

  • nodevectors

    Fastest network node embeddings in the west

  • The best candidates for it would be UMAP or graph embedding methods

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