style.css - Visualizing Uncertainty in Models using Pandas, Scikit Learn, Jinja, Flask, and D3.js

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style.css - Visualizing Uncertainty in Models using Pandas, Scikit Learn, Jinja, Flask, and D3.js

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Last edited Jul 10, 2019

Created on Jul 7, 2019

This example demonstrates how a web application presents the results of a machine-learning model, specifically focusing on visualizing prediction uncertainty for educational purposes. The interface, styled with CSS and using Bootstrap-like classes (e.g., `.card-deck`, `.box-shadow`), displays model output cards within a responsive layout. The application is built using Python libraries such as Pandas for data handling and Scikit-learn for model training, while the server-side is powered by Flask and Jinja2 templates to render dynamic HTML. Interactive visualization of the data points and uncertainty intervals is achieved using D3.js for client-side rendering, with the code referencing CSS properties for layout control rather than specific D3 API functions.

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