The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

The Unofficial Google Data Science Blog

APRIL 19, 2021

To explain, let’s borrow a quote from Nate Silver’s The Signal and the Noise : One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration. The numerical value of the signal became decoupled from the event it was measuring even as the ordinal value remained unchanged.

Modeling 122

Modeling IT Metrics Testing 122

DataRobot Blog

MARCH 9, 2022

The IMDB Movie Reviews Dataset provides 50,000 highly polarized movie reviews with a 50-50 train/test split. Whenever you test a machine learning method, it’s helpful to have a baseline method and accuracy level against which to measure improvements. It provides 1.6 Sentiment analysis, a baseline method. More advanced models.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

The Unofficial Google Data Science Blog

MAY 31, 2016

Similarly, we could test the effectiveness of a search ad compared to showing only organic search results. It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. days or weeks).

Advertising 43

Advertising Testing Sales Statistics 43

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. 2007): Propose a finite collection $mathcal L={hat e_k:k=1,ldots,K}$ of estimation algorithms. This is often referred to as the positivity assumption.

Statistics 45

Statistics Optimization Modeling Experimentation 45

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., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. Researchers and practitioners have been using human-labeled data for many years, trying to understand all sorts of abstract concepts that we could not measure otherwise. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Measuring Online Engagement: What Role Does Web Analytics Play? "Engagement" How Do I Measure Success?

KPI 125

KPI Metrics Measurement ROI 125

O'Reilly on Data

DECEMBER 9, 2019

We use the diagnostic test results of our regression model to support the reasons why CIs should not be used in financial data analyses. The probability of an event should be measured empirically by repeating similar experiments ad nauseam —either in reality or hypothetically. Frequently used interpretation of probability.

Statistics 31

Statistics Uncertainty Risk Marketing 31

Occam's Razor

FEBRUARY 7, 2011

Brian Krick: Best way to measure and communicate "available demand" from available channels (social, search, display) for forecast modeling. Additionally, it is exceptionally difficult to measure available demand because 1. Post Testing: Visits 30k. No accountability = very little desire to measure. and finally 3.

Analytics 94

Analytics Measurement Metrics Predictive Analytics 94

Occam's Razor

OCTOBER 15, 2012

I spend 70% of my time in the US and for those discussions I'm primary looking at speed (connection above), mobile penetration (yes, 2007 was the year of mobile!), It is SUPER important to passionately measure the offline impact of your online efforts until the day you die (cue, dramatic music). It is an intense amount of effort.

Advertising 137

Advertising Marketing Strategy Insurance 137

Edwin Chen

MARCH 13, 2011

This is a summary of Bell and Koren’s 2007 Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights paper. I’m now (very slowly) trying to clean up my notes and put them online. Eventually, I hope to have a more integrated tutorial, but here’s a rough draft for now.).

Optimization 40

Optimization Modeling Cost-Benefit Measurement 40

datapine

DECEMBER 28, 2021

To make sure the reliability is high, there are various techniques to perform – the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. These controlling measures are essential and should be part of any experiment or survey – unfortunately, that isn’t always the case.

Statistics 363

Statistics Advertising Visualization Data mining 363

CIO Business Intelligence

OCTOBER 29, 2024

since 2007. EA’s look at the entire “estate” with an enterprise-wide view and being inclusive in their approach to solutioning business asks while acknowledging the importance of taking sustainability measures and responsible AI practices into account. Measures progress in reducing outdated or redundant technology systems.

Enterprise 122

Enterprise Cost-Benefit Metrics Strategy 122