- 07-12, 09:30–11:00, Wicklow Hall 2A
- 07-12, 11:15–12:45, Wicklow Hall 2A
All times in Europe/Dublin
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.
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.
some
Expected audience expertise: Python –some
Abstract as a tweet –Inpsect and try to interpret your scikit-learn machine-learning models
I am a research engineer. I have a PhD in computer science and have been a scikit-learn core developer since 2017.