2007 Metrics Optimization Uncertainty 
The Unofficial Google Data Science Blog
APRIL 23, 2024
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., Crucially, it takes into account the uncertainty inherent in our experiments. Here, $X$ is a vector of tuning parameters that control the system's operating characteristics (e.g.
Occam's Razor
APRIL 8, 2013
To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.
The Unofficial Google Data Science Blog
APRIL 19, 2021
Calibration and other considerations Calibration is a desirable property, but it is not the only important metric. bar{pi} (1 - bar{pi})$: This is the irreducible loss due to uncertainty. isn’t good enough: it optimizes the calibration term, but pays the price in sharpness. And users may start receiving a lot more spam!
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