Results 21 to 30 of about 5,516,106 (266)
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests [PDF]
Journal of the American Statistical Association, 2015 Many scientific and engineering challenges—ranging from personalized medicine to customized marketing recommendations—require an understanding of treatment effect heterogeneity.
Stefan Wager, S. Athey
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ISPRS Int. J. Geo Inf., 2022 The aim of this paper is to present developments of an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data. We
S. Georganos, S. Kalogirou
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On Explaining Random Forests with SAT [PDF]
International Joint Conference on Artificial Intelligence, 2021 Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers. Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for computing explanations of RFs.
Yacine Izza, Joao Marques-Silva
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Sleep classification from wrist-worn accelerometer data using random forests
Scientific Reports, 2021 Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement.
Kalaivani Sundararajan +11 more
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A Random Forests Approach to Predicting Clean Energy Stock Prices
Journal of Risk and Financial Management, 2021 Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable
Perry Sadorsky
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Multi-Class Assessment Based on Random Forests
Education Sciences, 2021 Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their
Mehdi Berriri +3 more
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ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R [PDF]
, 2015 We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported.
Marvin N. Wright, A. Ziegler
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Random Forests for Spatially Dependent Data
Journal of the American Statistical Association, 2021 Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process (GP) distributed spatial random effect, are widely used for analyses of geospatial data. We consider the setting where the covariate effect is nonlinear.
Arkajyoti Saha, Sumanta Basu, A. Datta
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A Truly Spatial Random Forests Algorithm for Geoscience Data Analysis and Modelling
Mathematical Geosciences, 2021 Spatial data mining helps to find hidden but potentially informative patterns from large and high-dimensional geoscience data. Non-spatial learners generally look at the observations based on their relationships in the feature space, which means that ...
Hassan Talebi +3 more
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Aleatoric and Epistemic Uncertainty with Random Forests [PDF]
International Symposium on Intelligent Data Analysis, 2020 Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of ...
M. Shaker, Eyke Hüllermeier
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