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., Crucially, it takes into account the uncertainty inherent in our experiments. Here, $X$ is a vector of tuning parameters that control the system's operating characteristics (e.g.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. These characteristics of the problem drive the forecasting approaches.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Timo Elliott

JANUARY 18, 2021

We’ve created a metric with our data scientists called P0, the progress to zero, and we talk in terms of the progress to zero in terms of case counts and deaths. Data has always been important for organizations, but now, faced with so much uncertainty, everybody is interested in having more.

IT 71

IT Dashboards Digital Transformation Data-driven 71

Peter James Thomas

AUGUST 12, 2018

Pertinence and fidelity of metrics developed from Data. Metrics are seldom reliant on just one data element, but are often rather combinations. There are often compromises to be made in defining metrics. Integrity of statistical estimates based on Data. For example: Is this a good way to define New Business Growth?

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. They learned about a lot of process that requires that you get rid of uncertainty. They’re being told they have to embrace uncertainty.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

The Unofficial Google Data Science Blog

MAY 31, 2016

This means it is possible to specify exactly in which geos an ad campaign will be served – and to observe the ad spend and the response metric at the geo level. In other words, iROAS is the slope of a curve of the response metric plotted against the underlying advertising spend. They are non-overlapping geo-targetable regions.

Advertising 42

Advertising Testing Sales Statistics 42

Domino Data Lab

JUNE 23, 2019

Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples.

Management 110

Management Machine Learning Experimentation Metrics 110

Insight

FEBRUARY 19, 2020

Building Models to Predict Movie Profitability Here I use profitability as the metric of success for a film and define profitability as the return on investment (ROI). I used XGBoost as my regression model as I found that it slightly outperforms random forest regression when using the root-mean-square error (RMSE) as the goodness metric.

Risk 67

Risk ROI Modeling Metrics 67

The Unofficial Google Data Science Blog

APRIL 16, 2019

Such decisions involve an actual hypothesis test on specific metrics (e.g. Often, an established product will have an overall evaluation criterion (OEC) that incorporates trade-offs among important metrics and between short- and long-term success. The metrics to measure the impact of the change might not yet be established.

Experimentation 68

Experimentation Sales Metrics Measurement 68

datapine

SEPTEMBER 4, 2023

Not only is each flight color-coded by the airline, but this short movie-style visualization has transformed flight-based metrics into a piece of art that shows the path of each flight in action. A glorious example of numerical metrics transformed into something accessible, engaging, and informational for everyone.

Interactive 264

Interactive Visualization Cost-Benefit Dashboards 264

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Note also that this account does not involve ambiguity due to statistical uncertainty. The green and peach lines show metrics for users of the same device, on experiments that left the result fully visible, or moved it up by about the same amount.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45