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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. In their 2002 paper Chawla et al. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) Generation of artificial examples. return synthetic. Chawla et al.,

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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