2005, Interactive and Uncertainty

Using random effects models in prediction problems

The Unofficial Google Data Science Blog

MARCH 31, 2016

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. Column "a" is an advertiser id, "b" is a web site, and "c" is the 'interaction' of columns "a" and "b". $y$ ICML, (2005). [3] 2005): 301-320. [9]

Modeling

Modeling Statistics Advertising Testing

Data Science, Past & Future

Domino Data Lab

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. They’re being told they have to embrace uncertainty.

Data Science

Data Science Machine Learning Data Governance Modeling

Data Leaders Brief

Using random effects models in prediction problems

Data Science, Past & Future

Webinars

Stay Connected