Remove 2014 Remove Experimentation Remove Metrics Remove Statistics
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Although it’s not perfect, [Note: These are statistical approximations, of course!] At the time—in 2014—the three were colleagues working. We like the ROC AUC for two reasons: It blends together two useful metrics—true positive rate and false positive rate—into a single summary value. Example 11.6 Pennington, J., 0.85 = 0.15.