Remove 2015 Remove Knowledge Discovery Remove Testing
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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

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Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

One way to check $f_theta$ is to gather test data and check whether the model fits the relationship between training and test data. This tests the model’s ability to distinguish what is common for each item between the two data sets (the underlying $theta$) and what is different (the draw from $f_theta$).

KDD 40
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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

After forming the X and y variables, we split the data into training and test sets. 2015) for additional details. Next, we pick a sample that we want to get an explanation for, say the first sample from our test dataset (sample id 0). For sample 23 from the test set, the model is leaning towards a bad credit prediction.

Modeling 139