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The curse of Dimensionality

Domino Data Lab

The accuracy of any predictive model approaches 100%. Property 4: The accuracy of any predictive model approaches 100%. This means models can always be found that predict group characteristic with high accuracy. There should be no model to accurately predict even and odd rows with random data.

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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. You get a confusing little thing, but the visualization is interactive. The Treemap, Sunburst and Packedcircle demonstrate three possible paths you can take to go from a table to something much more understandable and much more interactive.

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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. Instead, we recommend using the bokeh library to create a highly interactive—and actionable—plot, as with the code provided in Example 11.11.

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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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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

They then proceeded to analyze three areas: the employee selection and onboarding, the daily staff management, and finally the employees’ behavior and interactions in the restaurants. Lieferando is a European online food-ordering service that was acquired by Just Eat Take Away in 2014.

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

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. layer-wise relevance propagation), model distillation (e.g. PDPs for the bicycle count prediction model (Molnar, 2009).

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