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

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). TIME – time points of measured pain score and plasma concentration (in hrs). References. [1] The analyses shown below are accessible in the NCA project on Domino’s trial site.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

The Unofficial Google Data Science Blog

JANUARY 14, 2016

In each case, users engage with the service at will and the service makes available a rich set of possible interactions. But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Ontotext

FEBRUARY 21, 2024

This dramatically simplifies the interaction with complex databases and analytics systems. Join us as we demystify the methodologies empowering such implementations, shed light on their range of capabilities, and detail how Ontotext is harnessing these technologies to bring transformative enhancements to our data interaction landscape.

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

Domino Data Lab

AUGUST 1, 2021

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139