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German Medical Science
Conference object . 2021
License: CC BY
Data sources: German Medical Science
https://dx.doi.org/10.3205/20g...
Conference object . 2021
License: CC BY
Data sources: Datacite
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Predictions by Random Forests – Confidence Intervals and their Coverage Probabilities

descriptionPublicationkeyboard_double_arrow_right Conference object 26 Feb 2021 English Publisher:German Medical Science GMS Publishing House
Authors: Kormilez, D; Laabs, BH; König, IR;

doi: 10.3205/20gmds185

Predictions by Random Forests – Confidence Intervals and their Coverage Probabilities

Abstract

Background: Random forests are a popular supervised learning method which were first proposed by Breiman [ref:1]. Their main purpose is the robust prediction of an outcome based on a learned set of rules. To evaluate the precision of predictions their scattering and distributions are important.[for full text, please go to the a.m. URL]

65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

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Keywords

ddc: 610, 610 Medical sciences; Medicine

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green