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. This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. Chawla et al. Indeed, in the original paper Chawla et al. References. link] Chawla, N.
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