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EuroPythonCode of ConductLive 📹

Inspect and try to interpret your scikit-learn machine-learning models

Room:
Wicklow Hall 2A
Start (Dublin time):
Start (your time):
Duration:
180 minutes

Abstract

This tutorial is subdivided into three parts.

First, we focus on the family of linear models and present the common pitfalls to be aware of when interpreting the coefficients of such models.

Then, we look at a larger range of models (e.g. gradient-boosting) and put into practice available inspection techniques developed in scikit-learn to inspect such models.

Finally, we present other tools to interpret models, not currently available in scikit-learn, but widely used. in practice.

TutorialPyData: Machine Learning, Stats

Description

Find the link to the material and minimal installation instructions: https://github.com/glemaitre/eupython_2022

This tutorial is subdivided into three parts.

First, we focus on the family of linear models and present the common pitfalls to be aware of when interpreting the coefficients of such models.

Then, we look at a larger range of models (e.g. gradient-boosting) and put into practice available inspection techniques developed in scikit-learn to inspect such models.

Finally, we present other tools to interpret models, not currently available in scikit-learn, but widely used. in practice.


The speaker

Guillaume Lemaitre

I am a research engineer. I have a PhD in computer science and have been a scikit-learn core developer since 2017.



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