ML internals: Synthetic Minority Oversampling (SMOTE) Technique
Domino Data Lab
MAY 20, 2021
In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. 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. References.
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