The Unofficial Google Data Science Blog

APRIL 19, 2021

by LEE RICHARDSON & TAYLOR POSPISIL Calibrated models make probabilistic predictions that match real world probabilities. 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.

Modeling 122

Modeling IT Metrics Testing 122

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., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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 95

Experimentation Statistics Testing Strategy 95

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. Another way of thinking about this is that we are simply building a regression model. I’m now (very slowly) trying to clean up my notes and put them online. Bob likes Kate Winslet).

Optimization 40

Optimization Modeling Cost-Benefit Measurement 40

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!), Think of it as attribution modeling. :). and mobile intent to purchase (I love this data). Most of the outcomes may still be offline. It is an intense amount of effort.

Advertising 136

Advertising Marketing Strategy Insurance 136

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. The first step in developing any model is gathering a suitable source of training data, and sentiment analysis is no exception. Sentiment analysis 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 42

Advertising Testing Sales Statistics 42

Occam's Razor

FEBRUARY 7, 2011

The best option is to hire a statistician with experience in data modeling and forecasting. 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.

Analytics 94

Analytics Measurement Metrics Predictive Analytics 94

O'Reilly on Data

DECEMBER 9, 2019

Recall from my previous blog post that all financial models are at the mercy of the Trinity of Errors , namely: errors in model specifications, errors in model parameter estimates, and errors resulting from the failure of a model to adapt to structural changes in its environment. For example, if a stock has a beta of 1.4

Statistics 27

Statistics Uncertainty Risk Marketing 27

Domino Data Lab

SEPTEMBER 25, 2019

We see it when working with log data, financial data, transactional data, and when measuring anything in a real engineering system. One of the most common ways of fitting time series models is to use either autoregressive (AR), moving average (MA) or both (ARMA). These models are well represented in R and are fairly easy to work with.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

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