15 Janvier - 21 Janvier


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Jeudi 18 Janvier
Heure: 10:30 - 11:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Rule-based machine learning via mathematical optimization
Description: Cristina Molero del Río Rule-based machine learning models are appealing because of their simple
decision structure. In this talk, we will present two examples, decision
trees and rule sets, with special focus on the former.

Contrary to classic classification and regression trees, built in a greedy
heuristic manner, designing the tree model through an optimization problem
allows us to easily include desirable properties in Machine Learning in
addition to prediction accuracy. We present a Non-Linear Optimization
approach that is scalable with respect to the size of the training sample,
and illustrate this flexibility to model several important issues in
Explainable and Fair Machine Learning. These include sparsity, as a proxy
for interpretability, by reducing the amount of information necessary to
predict well; fairness, by aiming to avoid predictions that discriminate
against sensitive features such as gender or race; the cost-sensitivity
for groups of individuals in which prediction errors are more critical,
such as patients of a disease, by ensuring an acceptable accuracy
performance for them; local explainability, where the goal is to identify
the predictor variables that have the largest impact on the individual
predictions; as well as data complexity in the form of observations of
functional nature. The performance of our approach is illustrated on real
and synthetic data sets