Introduction to scikit-learn: from model fitting to model interpretation EuroSciPy 2019

Introduction to scikit-learn: from model fitting to model interpretation
.ical

09-03, 14:00–15:30 (UTC), Track 2 (Baroja)

We will present scikit-learn by focusing on the available tools used to train a machine-learning model. Then, we will focus on the challenge linked to model interpretation and the available tools to understand these models.

Our introduction to scikit-learn will be subdivided into 2 parts.

We will give a general introduction to scikit-learn presenting basic concepts around cross-validation, pipeline estimator, and hyperparameter search.

Then, we will focus on model interpretation presenting the challenges and the available tools to understand a trained machine-learning model: partial independence plot, features importance, LIME, shapley values, etc.

Project Homepage / Git –

https://scikit-learn.org/stable/

Project Homepage / Git –

https://scikit-learn.org/stable/

Abstract as a tweet –

Scikit-learn introduction: fit, predicit and interpret

Python Skill Level –

basic

Domain Expertise –

some

Domains –

Data Visualisation, Machine Learning, Statistics