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pro vyhledávání: '"Bénard, Clément"'
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. In this article, we introduce a variable importance algorithm for DRFs, bas
Externí odkaz:
http://arxiv.org/abs/2310.12115
Autor:
Bénard, Clément, Josse, Julie
Causal random forests provide efficient estimates of heterogeneous treatment effects. However, forest algorithms are also well-known for their black-box nature, and therefore, do not characterize how input variables are involved in treatment effect h
Externí odkaz:
http://arxiv.org/abs/2308.03369
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradi
Externí odkaz:
http://arxiv.org/abs/2301.13528
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools. Shapley effects are now widely used to interpret both tree ensembles and neural network
Externí odkaz:
http://arxiv.org/abs/2105.11724
Variable importance measures are the main tools to analyze the black-box mechanisms of random forests. Although the mean decrease accuracy (MDA) is widely accepted as the most efficient variable importance measure for random forests, little is known
Externí odkaz:
http://arxiv.org/abs/2102.13347
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 learning algorithms are often referred to as "black boxes" because of t
Externí odkaz:
http://arxiv.org/abs/2004.14841
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. This lack of interpretability is a s
Externí odkaz:
http://arxiv.org/abs/1908.06852
Akademický článek
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Autor:
Bénard, Clément
Publikováno v:
Statistics [math.ST]. Sorbonne Université, 2021. English
This thesis deals with the interpretability of learning algorithms in an industrial context.Manufacturing production and the design of industrial systems are two examples whereinterpretability of learning methods enables to grasp how the inputs and o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2dc3c0f9b6b91d3e70aa10a4d760e537
https://tel.archives-ouvertes.fr/tel-03478241/document
https://tel.archives-ouvertes.fr/tel-03478241/document
Akademický článek
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