Understanding Machine Learning Through Visualizations with Benjamin Bengfort and Rebecca Bilbro

Summary

Machine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.

Preface

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• Your host as usual is Tobias Macey and today I’m interviewing Rebecca Bilbro and Benjamin Bengfort about Yellowbrick, a scikit extension to use visualizations for assisting with model selection in your data science projects.

Interview

• Introductions
• How did you get introduced to Python?
• Can you describe the use case for Yellowbrick and how the project got started?
• What is involved in visualizing scikit-learn models?
  • What kinds of information do the visualizations convey?
  • How do they aid in understanding what is happening in the models?



• How much direction does yellowbrick provide in terms of knowing which visualizations will be helpful in various circumstances?


• What does the workflow look like for someone using Yellowbrick while iterating on a data science project?


• What are some of the common points of confusion that your students encounter when learning data science and how has yellowbrick assisted in achieving understanding?


• How is Yellowbrick iplemented and how has the design changed over the lifetime of the project?


• What would be required to integrate with other visualization libraries and what benefits (if any) might that provide?
  • What about other ML frameworks?



• What are some of the most challenging or unexpected aspects of building and maintaining Yellowbrick?


• What are the limitations or edge cases for yellowbrick?


• What do you have planned for the future of yellowbrick?


• Beyond visualization, what are some of the other areas that you would like to see innovation in how data science is taught and/or conducted to make it more accessible?

Keep In Touch

• Rebecca Bilbro
  • Github
  • Twitter



• Benjamin Bengfort
  • Github
  • Twitter

Picks

• Tobias
  • Poutine



• Rebecca
  • The color yellow



• Benjamin
  • ALL CAPS

Links

• Hadoop
• Natural Language Processing
• Machine Learning
• scikit-learn
• Model Selection Triple
• the machine learning workflow
• scikit-yb
• Yellowbrick
• Visualizer API
• Visual Tests
• Jupyter
• Matplotlib
• Tensorflow
• Hyperparameter
• Parallel Coordinates
• Radviz
• Rank2D
• Prediction Error Plot
• Residuals Plot
• Validation Curves
• Alpha Selection
• Frequency Distribution Plot
• Bayes Theorem
• Seaborn
• Stop Words
• N-gram
• Craig – Bias and Fairness of Algorithms
• Shiny
• Bokeh
• Keras
• StatsModels
• Tensorboard
• PyTorch
• NumPy
• Voxel
• Wizard of Oz

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA