1 citations · 1 across the 2 of their papers we have counts for
4 papers
Leveraging Association Rules for Better Predictions and Better Explanations
Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis +2
We present a new approach to classification that combines data and knowledge. In this approach, data mining is used to derive association rules (possibly with negations) from data.…
A Rectification-Based Approach for Distilling Boosted Trees into Decision Trees
Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis +2
We present a new approach for distilling boosted trees into decision trees, in the objective of generating an ML model offering an acceptable compromise in terms of predictive perf…
Computing Abductive Explanations for Boosted Trees
Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis +1
Boosted trees is a dominant ML model, exhibiting high accuracy. However, boosted trees are hardly intelligible, and this is a problem whenever they are used in safety-critical appl…
PyCSP3: Modeling Combinatorial Constrained Problems in Python
Christophe Lecoutre, Nicolas Szczepanski
In this document, we introduce PyCSP, a Python library that allows us to write models of combinatorial constrained problems in a declarative manner. Currently, with PyCSP, yo…