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

AWS Big Data

MARCH 11, 2024

Make sure that data is flowing into your MSK topic using Amazon CloudWatch metrics (for example, BytesOutPerSec ). To avoid these types of issues, test the logic of your materialized view definition carefully; otherwise, land the records into the default VARBYTE column and process them later.

Analytics 65

Analytics Data Warehouse Optimization Metrics 65

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. In fact, Hainmueller (2012) show that entropy balancing is equivalent to estimating the weights as a log-linear model of the covariate functions $c_j(X)$.

Statistics 44

Statistics Optimization Modeling Experimentation 44

AWS Big Data

OCTOBER 19, 2023

Customers uploading and searching through decades of photos helped turn search into critical infrastructure, growing steadily since SmugMug first used Amazon CloudSearch in 2012, followed by Amazon OpenSearch Service since 2018, after reaching billions of documents and terabytes of search storage. Stay tuned for results.

Publishing 55

Publishing Testing Cost-Benefit Data-driven 55

Sisense

MAY 26, 2020

While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. Using the semantic meaning of words it already knows as a base, the model can understand the meanings of words it doesn’t know that appear in test data.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Cloudera

NOVEMBER 17, 2021

There’s recognition that it’s nearly impossible to find the unicorn data scientist that was the apple of every CEO’s eye in 2012. It enables the tracking of experiment metrics like loss and accuracy, visualization of a model’s graph, projection of embeddings to a lower dimensional space, and much more.

Machine Learning 122

Machine Learning Visualization Data Science Metrics 122

Domino Data Lab

APRIL 21, 2021

from sklearn import metrics. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Fraudulent transactions are 50.00% of the test set. Feature Engineering.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Occam's Razor

DECEMBER 3, 2012

And here's the test to apply to your brand: Would you want your brand's posts to in your own newsfeed every day? Another test to apply: How are you doing in terms of Conversation Rate, Amplification Rate, Applause Rate ? Use those metrics to optimize your social existence. Your mobile strategy is ready for 2012.

Marketing 147

Marketing Advertising Strategy Optimization 147

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. 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. They are non-overlapping geo-targetable regions.

Advertising 42

Advertising Testing Sales Statistics 42

DataRobot Blog

APRIL 4, 2022

This metric is important to discover the fluctuation in sales that is due to seasonal demand patterns versus the sales generated by pricing or media. Originating in game theory, there is a reason why Shapley Value earned Lloyd Shapley a Nobel Prize in 2012. Classical Modeling Considerations. Breakthrough #2: Shapley Value.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. What’s been the impact of using ML models on culture and organization? Who builds their models? How are decisions and priorities set and by whom within the organization?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Depict Data Studio

APRIL 10, 2023

Apply the Squint Test In these before scatter plot on the left, the cluttered appearance distracts us from the data. Apply the Squint Test. I like to test my drafts ahead of time to make sure they’ll still be legible even if they’re printed in grayscale. You can test your drafts a couple of different ways.

Visualization 105

Visualization Dashboards Testing Reporting 105

datapine

MAY 17, 2021

Without a doubt, the best way to drive maximum value from the metrics, insights, and information is through something called data discovery. Your Chance: Want to test a professional data discovery tool for free? Moreover, 83% of executives have pursued big data projects to gain a competitive edge. We offer a 14 day free trial.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161