2002, Data mining, Forecasting and Machine Learning

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. Learning wider regions improves the generalisation of the classifier, as the region of the minority class is not so tightly constrained by the observations in the majority. Generation of artificial examples.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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