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Conditional variable importance for random forests

Overview of attention for article published in BMC Bioinformatics, July 2008
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
2 policy sources
twitter
5 X users
googleplus
1 Google+ user
q&a
8 Q&A threads

Citations

dimensions_citation
2193 Dimensions

Readers on

mendeley
1872 Mendeley
citeulike
14 CiteULike
connotea
1 Connotea
Title
Conditional variable importance for random forests
Published in
BMC Bioinformatics, July 2008
DOI 10.1186/1471-2105-9-307
Pubmed ID
Authors

Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, Achim Zeileis

Abstract

Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,872 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 21 1%
United Kingdom 14 <1%
Germany 10 <1%
Spain 8 <1%
Canada 8 <1%
Switzerland 5 <1%
Australia 5 <1%
Brazil 4 <1%
Belgium 4 <1%
Other 35 2%
Unknown 1758 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 481 26%
Researcher 334 18%
Student > Master 274 15%
Student > Bachelor 114 6%
Student > Doctoral Student 101 5%
Other 241 13%
Unknown 327 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 313 17%
Environmental Science 214 11%
Computer Science 181 10%
Engineering 152 8%
Earth and Planetary Sciences 90 5%
Other 481 26%
Unknown 441 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 48. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2022.
All research outputs
#877,013
of 25,470,300 outputs
Outputs from BMC Bioinformatics
#59
of 7,705 outputs
Outputs of similar age
#1,780
of 95,830 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 41 outputs
Altmetric has tracked 25,470,300 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,705 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 95,830 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.