Introduction to scikit-learn: from model fitting to model interpretation
2019-09-03, 14:00–15:30, 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.


Domains – Data Visualisation, Machine Learning, Statistics Project Homepage / Git – https://scikit-learn.org/stable/ Domain Expertise – some Python Skill Level – basic Project Homepage / Git – https://scikit-learn.org/stable/ Abstract as a tweet – Scikit-learn introduction: fit, predicit and interpret