599 citations · 862 across the 35 of their papers we have counts for
5 papers · 1 filter
Analyzing the tree-layer structure of Deep Forests
Ludovic Arnould, Claire Boyer, Erwan Scornet +1
Random forests on the one hand, and neural networks on the other hand, have met great success in the machine learning community for their predictive performance. Combinations of bo…
NeuMiss networks: differentiable programming for supervised learning with missing values
Marine Le Morvan, Julie Josse, Thomas Moreau +2
The presence of missing values makes supervised learning much more challenging. Indeed, previous work has shown that even when the response is a linear function of the complete dat…
Interpretable Random Forests via Rule Extraction
Clément Bénard, Gérard Biau, Sébastien da Veiga +1
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art l…
Linear predictor on linearly-generated data with missing values: non consistency and solutions
Marine Le Morvan, Nicolas Prost, Julie Josse +2
We consider building predictors when the data have missing values. We study the seemingly-simple case where the target to predict is a linear function of the fully-observed data an…
Trees, forests, and impurity-based variable importance
Erwan Scornet
Tree ensemble methods such as random forests [Breiman, 2001] are very popular to handle high-dimensional tabular data sets, notably because of their good predictive accuracy. Howev…