Results 31 to 40 of about 5,516,106 (266)
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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The Macroeconomy as a Random Forest [PDF]
SSRN Electronic Journal, 2020 SummaryI develop the macroeconomic random forest (MRF), an algorithm adapting the canonical machine learning (ML) tool, to flexibly model evolving parameters in a linear macro equation. Its main output, generalized time‐varying parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, and ...
openaire +3 more sources
Evaluation of random forests and Prophet for daily streamflow forecasting [PDF]
Advances in Geosciences, 2018 We assess the performance of random forests and Prophet in forecasting daily streamflow up to seven days ahead in a river in the US. Both the assessed forecasting methods use past streamflow observations, while random forests additionally use past ...
G. A. Papacharalampous +3 more
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Improving random forest predictions in small datasets from two-phase sampling designs
BMC Medical Informatics and Decision Making, 2021 Background While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases—a common situation in ...
Sunwoo Han +2 more
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Unbiased variable importance for random forests [PDF]
Communications in Statistics - Theory and Methods, 2020 The default variable-importance measure in random forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting criterion.
Markus Loecher
semanticscholar +1 more source
Random two-component spanning forests [PDF]
, 2014 We study random two-component spanning forests ($2$SFs) of finite graphs, giving formulas for the first and second moments of the sizes of the components, vertex-inclusion probabilities for one or two vertices, and the probability that an edge separates ...
Kassel, Adrien, Kenyon, Richard, Wu, Wei
core +1 more source
A Multi-Task Framework for Action Prediction
Information, 2020 Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different ...
Tianyu Yu +3 more
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Splitting on categorical predictors in random forests [PDF]
PeerJ, 2019 One reason for the widespread success of random forests (RFs) is their ability to analyze most datasets without preprocessing. For example, in contrast to many other statistical methods and machine learning approaches, no recoding such as dummy coding is
Marvin N. Wright, Inke R. König
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Bioinformatics, 2008 Abstract Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly.
Dhammika Amaratunga +2 more
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Non-unitarisable representations and random forests [PDF]
, 2008 We establish a connection between Dixmier's unitarisability problem and the expected degree of random forests on a group. As a consequence, a residually finite group is non-unitarisable if its first L2-Betti number is non-zero or if it is finitely ...
Epstein, Inessa, Monod, Nicolas
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