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PyCon 2019 | Open the Black Box: An Introduction to Model Interpretability in Python

  • root
  • July 29, 2019
  • 1 minute read

PyCon 2019 | Open the Black Box: An Introduction to Model Interpretability in Python


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Speaker: Kevin Lemagnen

 

What’s the use of sophisticated machine learning models if you can’t interpret them?

In fact, many industries including finance and healthcare require clear explanations of why a decision is made. This tutorial covers recent model interpretability techniques that are essentials in your data scientist toolbox: Eli5, LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations).

You will learn how to apply these techniques in Python on real-world data science problems in order to debug your models and explain their decisions.

You will also learn the conceptual background behind these techniques so you can better understand when they are appropriate.

 

Slides can be found at: https://speakerdeck.com/pycon2019 and https://github.com/PyCon/2019-slides

Read More  PyCon 2019 | Writing About Python (Even When You Hate Writing)

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Related Topics
  • Machine Learning
  • PyCon
  • Python
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