ML internals: Synthetic Minority Oversampling (SMOTE) Technique
Domino Data Lab
MAY 20, 2021
Machine Learning algorithms often need to handle highly-imbalanced datasets. In their 2002 paper Chawla et al. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling. Chawla et al.,
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