2001, Data Collection, Knowledge Discovery and Presentation

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

We present the inner workings of the SMOTE algorithm and show a simple “from scratch” implementation of SMOTE. 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. A word of caution.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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