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.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Domino Data Lab

DECEMBER 4, 2020

TIME – time points of measured pain score and plasma concentration (in hrs). As each dose is administered at TIME=0 (the other entries are times of concentration and pain measurement), we create an AMT column as follows: pain_df[:"AMT"] = ifelse.(pain_df.TIME.== References. [1] and 3 to 8 hours. pain_df.TIME.==

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

The Unofficial Google Data Science Blog

JANUARY 14, 2016

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. Being dimensionless, it is a simple measure of the variability of a (non-negative) random variable.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. The capacity and performance of supercomputers is measured with the so-called FLOPS (floating point operations per second). What are supercomputers and why do we need them?

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

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.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Domino Data Lab

MAY 20, 2021

This renders measures like classification accuracy meaningless. References. The use of multiple measurements in taxonomic problems. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Banko, M., & Brill, E. Machine Learning, 57–78. Dua, D., & Graff, C.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

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].

KDD 40

KDD Testing Machine Learning Modeling 40