599 citations · 862 across the 35 of their papers we have counts for
3 papers · 1 filter
SIRUS: Stable and Interpretable RUle Set for Classification
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involv…
AMF: Aggregated Mondrian Forests for Online Learning
Jaouad Mourtada, Stéphane Gaïffas, Erwan Scornet
Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes…
On the consistency of supervised learning with missing values
Julie Josse, Jacob M. Chen, Nicolas Prost +2
In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating…