ML internals: Synthetic Minority Oversampling (SMOTE) Technique
Domino Data Lab
MAY 20, 2021
Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large. In their 2002 paper Chawla et al. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling.
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