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.