Sampling design optimization for soil mapping with random forest
Abstract Machine learning techniques are widely employed to generate digital soil maps.
The map accuracy is partly determined by the number and spatial locations of the …
The map accuracy is partly determined by the number and spatial locations of the …
A debiased MDI feature importance measure for random forests
Tree ensembles such as Random Forests have achieved impressive empirical success
across a wide variety of applications. To understand how these models make predictions …
across a wide variety of applications. To understand how these models make predictions …
Unraveling correlations between molecular properties and device parameters of organic solar cells using machine learning
H Sahu, H Ma - The journal of physical chemistry letters, 2019 - ACS Publications
Understanding the relationships between molecular properties and device parameters is
highly desired not only to improve the overall performance of an organic solar cell but also to …
highly desired not only to improve the overall performance of an organic solar cell but also to …
Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records
Purpose To predict the need for surgical intervention in patients with primary open-angle
glaucoma (POAG) using systemic data in electronic health records (EHRs). Design …
glaucoma (POAG) using systemic data in electronic health records (EHRs). Design …
Identification of discriminatory antibiotic resistance genes among environmental resistomes using extremely randomized tree algorithm
Background The interconnectivities of built and natural environments can serve as conduits
for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies …
for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies …
Machine learning prediction on properties of nanoporous materials utilizing pore geometry barcodes
X Zhang, J Cui, K Zhang, J Wu… - Journal of chemical …, 2019 - ACS Publications
In this work, we propose a computational framework for machine learning prediction on
structural and performance properties of nanoporous materials for methane storage …
structural and performance properties of nanoporous materials for methane storage …
Surrogate minimal depth as an importance measure for variables in random forests
S Seifert, S Gundlach, S Szymczak - Bioinformatics, 2019 - academic.oup.com
Motivation It has been shown that the machine learning approach random forest can be
successfully applied to omics data, such as gene expression data, for classification or …
successfully applied to omics data, such as gene expression data, for classification or …
[PDF][PDF] Package 'ranger'
Description A fast implementation of Random Forests, particularly suited for high
dimensional data. Ensembles of classification, regression, survival and probability prediction …
dimensional data. Ensembles of classification, regression, survival and probability prediction …
Personalised analytics for rare disease diagnostics
Whole genome and exome sequencing is a standard tool for the diagnosis of patients
suffering from rare and other genetic disorders. The interpretation of the tens of thousands of …
suffering from rare and other genetic disorders. The interpretation of the tens of thousands of …
Unbiased measurement of feature importance in tree-based methods
We propose a modification that corrects for split-improvement variable importance measures
in Random Forests and other tree-based methods. These methods have been shown to be …
in Random Forests and other tree-based methods. These methods have been shown to be …