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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

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. A/B testing is used widely in information technology companies to guide product development and improvements.

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Performing Non-Compartmental Analysis with Julia and Pumas AI

Domino Data Lab

Once all packages have been imported, we can move on to loading our test data. TIME – time points of measured pain score and plasma concentration (in hrs). There are individual NCA functions that allow us to manually calculate the specific pharmacokinetic measurement of interest. and 3 to 8 hours. pain_df.TIME.==

Metrics 59
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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

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. For this purpose, let’s assume we use a t-test for difference between group means.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

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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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

This renders measures like classification accuracy meaningless. Their tests are performed using C4.5-generated note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). References. The use of multiple measurements in taxonomic problems.

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

The Unofficial Google Data Science Blog

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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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. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. Next, we pick a sample that we want to get an explanation for, say the first sample from our test dataset (sample id 0). References.

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