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IT leaders adjust budget priorities as economic outlook shifts

CIO Business Intelligence

We haven’t changed our forecast in three quarters,” he says, noting that the US gross domestic product (GDP) is, technically, already in recession territory and has been for the past six months. Focus on risk management, he advises, and “have a little faith in your CFO and CEO. Gartner predicts 2023 IT spending will grow 5.1%

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., Chawla et al. References. link] Chawla, N.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

” “Data science” was first used as an independent discipline in 2001. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. Both data science and machine learning are used by data engineers and in almost every industry.

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Reclaiming the stories that algorithms tell

O'Reilly on Data

In 2001, just as the Lexile system was rolling out state-wide, a professor of education named Stephen Krashen took to the pages of the California School Library Journal to raise an alarm. His system was needed because “beginning teachers and librarians” were less expert at “forecasting comprehension rates” than the algorithm was.

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