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The curse of Dimensionality

Domino Data Lab

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. In high dimensions the data assumptions needed for statistical testing are not met. The accuracy of any predictive model approaches 100%.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

At the time—in 2014—the three were colleagues working. GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. Pennington, J.,

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated. This methodology of “test, look at the data, adjust” is at the heart and soul of business intelligence. Lieferando is a European online food-ordering service that was acquired by Just Eat Take Away in 2014.

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Data Science at The New York Times

Domino Data Lab

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

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Data Visualization Inspiration: Analysis To Insights To Action, Faster!

Occam's Razor

Short story #2: Predictive Modeling, Quantifying Cost of Inaction. Short story #2: Predictive Modeling, Quantifying Cost of Inaction. The work of the New York Times team inspired me it to do some predictive modeling for inaction in our world of digital marketing. Thank goodness for predictive models.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

Joint training, for example, adds an additional “explanation task” to the original problem and trains the system to solve the two “jointly” (see Bahdanau, 2014). In this article we’ll use Skater , a freely available framework for model interpretation, to illustrate some of the key concepts above.

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