2001, Data Collection, Metrics and Risk

Data Science, Past & Future

Domino Data Lab

JULY 22, 2019

What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things. You see these drivers involving risk and cost, but also opportunity. I can point to the year 2001. All righty. Where did this happen?

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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. Chawla et al.

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Machine Learning Metrics Data mining Knowledge Discovery

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Data Science, Past & Future

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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