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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

MARCH 31, 2016

Far from hypothetical, we have encountered these issues in our experiences with "big data" prediction problems. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. ICML, (2005). [3] 2005): 301-320. [9]

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JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. That was the origin of big data.

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Using random effects models in prediction problems

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