2001, 2009, Modeling and Visualization

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. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. References. Banko, M., & Brill, E. Scaling to very very large corpora for natural language disambiguation.

Machine Learning

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Data Science at The New York Times

Domino Data Lab

JULY 9, 2019

When he retired in 2009 he had some time on his hands. In 2001, Bill Cleveland writes this article saying, “You are doing it wrong.” One of the ways I frame that is, “Are you looking to build a predictive model? or a prescriptive model? or a descriptive model?”

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Data Leaders Brief

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Data Science at The New York Times

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