Today we close out our PyDataSci series joined by Rebecca Bilbro, head of data science at ICX media and co-creator of the popular open-source visualization library YellowBrick.
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In our conversation, Rebecca details her relationship with toolmaking, which led to the eventual creation of Yellowbrick, and gives us context to how it was built. We discuss popular tools within Yellowbrick, including a summary of their unit testing approach, some of the more interesting use cases that she’s seen over time, and the growth she’s seen in the community of contributors and examples of their contributions as they approach the release of Yellowbrick 1.0. Finally, we discuss how Yellowbrick can assist with model explainability.
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Thanks to our Sponsor!
I’d like to send a huge thanks to our sponsor for this series, IBM. IBM has a long history of engaging in and supporting open source projects that are important to enterprise data science — projects like Hadoop, Spark, Jupyter, Kubernetes, and Kubeflow.
IBM also hosts the IBM Data Science Community, which is a place for enterprise data scientists looking to learn, share, and engage with their peers and industry renowned practitioners. There you’ll find informative tutorials and case studies, Q&As with leaders in the field, and a lively forum covering a variety of topics of interest to beginning and experienced data scientists.
Visit the IBM Data Science Community at ibm.com/community/datascience.
From the Interview
- Natural Language Toolkit
- Paper: Model Selection Management Systems: The Next Frontier of Advanced Analytics
- YellowBrick Twitter
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“More On That Later” by Lee Rosevere licensed under CC By 4.0