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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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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) Their tests are performed using C4.5-generated

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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