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Found 7 papers, 3 papers with code
MMD-based Variable Importance for Distributional Random Forest
Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables.
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
In this article, we develop a new importance variable algorithm for causal forests, to quantify the impact of each input on the heterogeneity of treatment effects.
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC).
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools.
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests.
Interpretable Random Forests via Rule Extraction
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.
SIRUS: Stable and Interpretable RUle Set for Classification
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 involved in their prediction mechanism.
