A comparison of random forest variable selection methods for classification prediction modeling
JL Speiser, ME Miller, J Tooze, E Ip - Expert systems with applications, 2019 - Elsevier
Random forest classification is a popular machine learning method for developing prediction
models in many research settings. Often in prediction modeling, a goal is to reduce the …
models in many research settings. Often in prediction modeling, a goal is to reduce the …
Advances in global bioavailable strontium isoscapes
CP Bataille, BE Crowley, MJ Wooller… - Palaeogeography …, 2020 - Elsevier
Abstract Strontium isotope ratios (87 Sr/86 Sr) are a popular tool in provenance applications
in archeology, forensics, paleoecology, and environmental sciences. Using bioavailable 87 …
in archeology, forensics, paleoecology, and environmental sciences. Using bioavailable 87 …
Life history strategies of soil bacterial communities across global terrestrial biomes
The life history strategies of soil microbes determine their metabolic potential and their
response to environmental changes. Yet these strategies remain poorly understood. Here …
response to environmental changes. Yet these strategies remain poorly understood. Here …
Global models and predictions of plant diversity based on advanced machine learning techniques
Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical
cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering …
cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering …
Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
S Joel, PW Eastwick, CJ Allison… - Proceedings of the …, 2020 - National Acad Sciences
Given the powerful implications of relationship quality for health and well-being, a central
mission of relationship science is explaining why some romantic relationships thrive more …
mission of relationship science is explaining why some romantic relationships thrive more …
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …
service platform, which can incentivize vacant cars moving from over-supply regions to over …
Evaluation of variable selection methods for random forests and omics data sets
F Degenhardt, S Seifert… - Briefings in bioinformatics, 2019 - academic.oup.com
Abstract Machine learning methods and in particular random forests are promising
approaches for prediction based on high dimensional omics data sets. They provide …
approaches for prediction based on high dimensional omics data sets. They provide …
Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the Polish Carpathians
Abstract Information about forest stand species distribution is essential for biodiversity
modelling, forest disturbances, fire hazard and drought monitoring, biomass and carbon …
modelling, forest disturbances, fire hazard and drought monitoring, biomass and carbon …
Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling
Abstract Machine learning algorithms such as Random Forest (RF) are being increasingly
applied on traditionally geographical topics such as population estimation. Even though RF …
applied on traditionally geographical topics such as population estimation. Even though RF …