Results 11 to 20 of about 2,068,010 (305)
Random Prism: An Alternative to Random Forests [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach.
Stahl, F., Bramer, Max
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Conditional variable importance for random forests [PDF]
BMC Bioinformatics, 2008 Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables.
Augustin Thomas +4 more
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Predicting equity market price impact with performance weighted ensembles of random forests [PDF]
, 2014 For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events ...
Enrico Gerding +5 more
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Functional random forests for curve response
Scientific Reports, 2021 The rapid advancement of functional data in various application fields has increased the demand for advanced statistical approaches that can incorporate complex structures and nonlinear associations.
Guifang Fu, Xiaotian Dai, Yeheng Liang
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Expert Systems with Applications, 2023 In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision.
Hoarau, Arthur +3 more
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EDRN/random-decision-forests: 1.0.0
, 2021 This is the initial "general availability" (GA) or "first customer ship" (FCS) of Random Decision ...
Sean Kelly
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Using random forests to model 90-day hometime in people with stroke
BMC Medical Research Methodology, 2021 Background Ninety-day hometime, the number of days a patient is living in the community in the first 90 after stroke, exhibits a non-normal bucket-shaped distribution, with lower and upper constraints making its analysis difficult.
Jessalyn K. Holodinsky +3 more
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Machine Learning, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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JalalAl-Tamimi/Intro-Random-Forests: first release
, 2022 This repo contains material for a workshop on Random Forests in phonetics/phonology ...
Jalal Al-Tamimi
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