Results 1 to 10 of about 1,459,822 (215)

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, Lucas Mentch, Bradley J. Wheeler, Amanda L. Tapia, Marc Richards, Siyu Zhou, Lixia Yi, Susan Redline, Daniel J. Buysse   +8 more
doaj   +2 more sources

Variable Importance Measures Suggest Paramount Influence of Human Economics on Alien‐Species Introductions [PDF]

Ecology and Evolution
Identifying the most important variables that determine patterns and processes is one of the main goals in many scientific fields, including ecological and evolutionary studies.
Ignasi Arranz, Ralph Mac Nally, Emili García‐Berthou   +2 more
doaj   +2 more sources

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

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

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

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
doaj   +1 more source

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

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning

Journal of Causal Inference, 2022
Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of ...
Jin Yutong, Benkeser David
doaj   +1 more source

Investigating the Effects of Cell Size in Statistical Landslide Susceptibility Modelling for Different Landslide Typologies: A Test in Central–Northern Sicily

Applied Sciences, 2023
Optimally sizing grid cells is a relevant research issue in landslide susceptibility evaluation. In fact, the size of the adopted mapping units influences several aspects spanning from statistical (the number of positive/negative cases and prevalence and
Chiara Martinello, Chiara Cappadonia, Edoardo Rotigliano   +2 more
doaj   +1 more source