Results 1 to 10 of about 4,433,867 (193)
Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction [PDF]
BMC Medical Research Methodology, 2023 Background Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine.
Meredith L. Wallace +8 more
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Variable importance-weighted Random Forests. [PDF]
Quant Biol, 2017 BackgroundRandom Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest
Liu Y, Zhao H.
europepmc +4 more sources
Correlation and variable importance in random forests [PDF]
Statistics and Computing, 2016 This paper is about variable selection with the random forests algorithm in presence of correlated predictors. In high-dimensional regression or classification frameworks, variable selection is a difficult task, that becomes even more challenging in the ...
Gregorutti, Baptiste +2 more
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An AUC-based permutation variable importance measure for random forests. [PDF]
BMC Bioinformatics, 2013 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
Janitza S, Strobl C, Boulesteix AL.
europepmc +5 more sources
Variable importance and prediction methods for longitudinal problems with missing variables. [PDF]
PLoS One, 2015 We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use
Díaz I, Hubbard A, Decker A, Cohen M.
europepmc +7 more sources
Variable Importance Scores [PDF]
Journal of Data Science, 2021 29 pages, 13 ...
Wei-Yin Loh, Peigen Zhou
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Partial dependence through stratification
Machine Learning with Applications, 2021 Partial dependence curves (FPD) are commonly used to explain feature importance once a supervised learning model has been fitted to data. However, it is common for the same partial dependence algorithm to give meaningfully different curves for different ...
Terence Parr, James D. Wilson
doaj +1 more source
Decorrelated Variable Importance
J. Mach. Learn. Res., 2021 Because of the widespread use of black box prediction methods such as random forests and neural nets, there is renewed interest in developing methods for quantifying variable importance as part of the broader goal of interpretable prediction. A popular approach is to define a variable importance parameter - known as LOCO (Leave Out COvariates) - based ...
Isabella Verdinelli, Larry A. Wasserman
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Drones, 2022 The development of UAV sensors has made it possible to obtain a diverse array of spectral images in a single flight. In this study, high-resolution UAV-derived images of urban areas were employed to create land cover maps, including car-road, sidewalk ...
Geonung Park +3 more
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Applied Sciences, 2022 The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data.
Do-Hyun Lee +3 more
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