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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

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. In their 2002 paper Chawla et al. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling.

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Why Easier Governance Is Superior Governance

Alation

Today organizations view data as the “new oil”, an asset that, if used wisely, can support innovation while providing a meaningful competitive advantage and a better customer experience. And with data collection and replication growing so quickly, governance is more important than ever.

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Unintentional data

The Unofficial Google Data Science Blog

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.