Results 101 to 110 of about 5,516,106 (266)
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
, 2021 Random Forests (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construction of a kernel-type weighting function on training data, which can also ...
Bühlmann, Peter +4 more
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Evaluation of variable selection methods for random forests and omics data sets
Briefings Bioinform., 2017 Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power.
F. Degenhardt, S. Seifert, S. Szymczak
semanticscholar +1 more source
Formal Hypothesis Tests for Additive Structure in Random Forests
, 2016 While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference.
Hooker, Giles, Mentch, Lucas
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Classification of PolSAR Images by Stacked Random Forests
ISPRS International Journal of Geo-Information, 2018 This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor ...
Ronny Hänsch, Olaf Hellwich
doaj +1 more source
PLoS ONE, 2015 High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development.
F. Stevens +3 more
semanticscholar +1 more source
Analysis of purely random forests bias [PDF]
, 2014 Random forests are a very effective and commonly used statistical method, but their full theoretical analysis is still an open problem. As a first step, simplified models such as purely random forests have been introduced, in order to shed light on the ...
Arlot, Sylvain, Genuer, Robin
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The Problem of Redundant Variables in Random Forests
Acta Universitatis Lodziensis. Folia Oeconomica, 2018 Random forests are currently one of the most preferable methods of supervised learning among practitioners. Their popularity is influenced by the possibility of applying this method without a time consuming pre processing step. Random forests can be used
Mariusz Kubus
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Latin American Journal of Probability and Mathematical StatisticsMotivated by online recommendation systems, we study a family of random forests. The vertices of the forest are labeled by integers. Each non-positive integer $i\le 0$ is the root of a tree. Vertices labeled by positive integers $n \ge 1$ are attached sequentially such that the parent of vertex $n$ is $n-Z_n$, where the $Z_n$ are i.i.d.\ random ...
Broutin, Nicolas +3 more
openaire +2 more sources
Processes on Unimodular Random Networks
, 2018 We investigate unimodular random networks. Our motivations include their characterization via reversibility of an associated random walk and their similarities to unimodular quasi-transitive graphs.
Aldous, David, Lyons, Russell
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Time reversal dualities for some random forests
, 2014 We consider a random forest $\mathcal{F}^*$, defined as a sequence of i.i.d. birth-death (BD) trees, each started at time 0 from a single ancestor, stopped at the first tree having survived up to a fixed time $T$.
Felipe, Miraine Dávila, Lambert, Amaury
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