2001, Data Collection and Metrics

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. 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.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Science, Past & Future

Domino Data Lab

JULY 22, 2019

You know, typically, when you think about running projects, running teams, in terms of setting the priorities for projects, in terms of describing, what are the key metrics for success for a project, that usually falls on product management. I can point to the year 2001. Then also, it was the heyday for data warehousing and BI.

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Data Science Machine Learning Data Governance Modeling

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Data Science, Past & Future

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