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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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Banzhaf random forests: Cooperative game theory based random forests with consistency [PDF]
Neural Networks, 2018 Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory.
Jianyuan Sun +3 more
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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
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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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Analyzing Household Expenditures with Generalized Random Forests
Cauchy: Jurnal Matematika Murni dan AplikasiThis study investigates the performance of Generalized Random Forest (GRF), which has been known to be useful in understanding heterogeneous treatment effects (HTE) and non-linear relationships in high-dimensional data.
Eriski Isnanda +2 more
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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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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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Markov Network Structure Learning via Ensemble-of-Forests Models [PDF]
, 2013 Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models.
Arvaniti, Eirini, Claassen, Manfred
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Oblique random survival forests
The Annals of Applied Statistics, 2019 We introduce and evaluate the oblique random survival forest (ORSF). The ORSF is an ensemble method for right-censored survival data that uses linear combinations of input variables to recursively partition a set of training data. Regularized Cox proportional hazard models are used to identify linear combinations of input variables in each recursive ...
Jaeger, Byron C. +8 more
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