Remove 2001 Remove Data Collection Remove Machine Learning Remove Measurement
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A history of tech adaptation for today’s changing business needs

CIO Business Intelligence

“The company has been on a continuous journey to adapt its internal and external processes to new business needs and opportunities since 2001.” The first was becoming one of the first research companies to move its panels and surveys online, reducing costs and increasing the speed and scope of data collection.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

Machine Learning algorithms often need to handle highly-imbalanced datasets. This renders measures like classification accuracy meaningless. This in turns makes the performance evaluation of the classifier difficult, and can also harm the learning of an algorithm that strives to maximise accuracy. Machine Learning, 57–78.