2017, Data Collection, Forecasting and Knowledge Discovery

On the Hunt for Patterns: from Hippocrates to Supercomputers

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. Thanks to their might, now scientists and practitioners can develop innovative ways of collecting, storing, processing, and, ultimately, finding patterns in data.

Knowledge Discovery

Knowledge Discovery Experimentation Data-driven Metadata

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

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. note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017).

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Data Leaders Brief

On the Hunt for Patterns: from Hippocrates to Supercomputers

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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