Results 1 to 10 of about 16,649 (216)

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
openaire   +3 more sources

Analyzing Impact of Types of UAV-Derived Images on the Object-Based Classification of Land Cover in an Urban Area

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, Kyunghun Park, Bonggeun Song, Hungkyu Lee   +3 more
doaj   +1 more source

Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction

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, Lucas Mentch, Bradley J. Wheeler, Amanda L. Tapia, Marc Richards, Siyu Zhou, Lixia Yi, Susan Redline, Daniel J. Buysse   +8 more
doaj   +1 more source

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

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, Saem-Ee Woo, Min-Woong Jung, Tae-Young Heo   +3 more
doaj   +1 more source

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda.

Geospatial Health, 2023
As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions.
Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian   +6 more
doaj   +1 more source

The Importance of Being Variable [PDF]

The Journal of Neuroscience, 2011
New work suggests that blood oxygen level-dependent (BOLD) signal variability can be a much more powerful index of human age than mean activation, and that older brains are actually less variable than younger brains. However, little is known of how BOLD variability and task performance may relate.
Douglas D, Garrett, Natasa, Kovacevic, Anthony R, McIntosh, Cheryl L, Grady   +3 more
openaire   +2 more sources

Recursive Partitioning Methods to Examine the Effects of Physical Activity on Physical and Mental Health: A Case Study at One Public University in the United States

Physical Activity and Health, 2021
The purpose of this study was to examine the effects of physical activity (PA) on physical and mental health within an adult sample, while controlling for demographic characteristics. Data were analyzed from the 2018–2019 Exercise is Medicine® On Campus (
Samantha Robinson, Jessica Gilmore
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Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices

Forecasting, 2022
In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among ...
Silvia Golia, Luigi Grossi, Matteo Pelagatti   +2 more
doaj   +1 more source