Photo by Carlos Muza on Unsplash

Have you ever seen a data visualization and thought it would be great to use it in a Jupyter Notebook? I know I have, many times. Notebooks are the perfect place to experiment with data visualization, because they can contain all the data wrangling, preprocessing, pictures, and documentation in the same place. However, adding JavaScript to a Python notebook requires a lot of boilerplate code, which can become cumbersome and repetitive. To make matters worse, different Notebook environments have incompatible APIs for message passing between Python and JavaScript. …


How to open the black box of AutoML

Computer with code in the background.
Computer with code in the background.
Image credit: Negative Space

Building machine learning pipelines is often a difficult, time-consuming and trial-and-error task. AutoML makes this process easier, by automatically selecting computational steps, tuning hyperparameters and training end-to-end models that solve a machine learning problem. Auto-Sklearn¹ is one of the most popular open source AutoML systems. Given a dataset, a problem type (classification or regression) and a metric score, Auto-Sklearn is able to produce ensemble pipelines that optimize the chosen metric and produce good results.

Take, for example, the Digits dataset, where we want to classify images of numbers. …

Jorge Piazentin Ono

PhD Candidate at NYU & Data Vis Person — https://vgc.poly.edu/~jhenrique/

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