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. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al. References.
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