Data Collection, Forecasting, Knowledge Discovery and Measurement

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

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. Machine Learning, 57–78.

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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