The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning.

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The Unofficial Google Data Science Blog

JANUARY 14, 2016

And an LSOS is awash in data, right? Well, it turns out that depending on what it cares to measure, an LSOS might not have enough data. The practical consequence of this is that we can’t afford to be sloppy about measuring statistical significance and confidence intervals. known, equal variances). An effect size of 0.2

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The Unofficial Google Data Science Blog

JULY 22, 2020

This post considers a common design for an OCE where a user may be randomly assigned an arm on their first visit during the experiment, with assignment weights referring to the proportion that are randomly assigned to each arm. For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation.

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Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. References. link] Ling, C.

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The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Posteriors are useful to understand the system, measure accuracy, and make better decisions. Methods like the Poisson bootstrap can help us measure the variability of $t$, but don’t give us posteriors either, particularly since good high-dimensional estimators aren’t unbiased. For more on ad CTR estimation, refer to [2].

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The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

At Google, we tend to refer to them as slices. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect. Let’s go back to our example of measuring the fraction of user sessions with purchase. Let $Y_i$ be the response measured on the $i$th user session.

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Domino Data Lab

AUGUST 1, 2021

but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. Instead, you should focus on how techniques like PDPs and LIME can be used to gain insights into the model’s inner workings and how you can add those to your data science toolbox. References. See Wei et al.

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