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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 (96th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
5 tweeters
googleplus
1 Google+ user
q&a
3 Q&A threads

Citations

dimensions_citation
1515 Dimensions

Readers on

mendeley
1543 Mendeley
citeulike
14 CiteULike
connotea
1 Connotea
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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.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 1,543 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%
Canada 8 <1%
Spain 8 <1%
Australia 5 <1%
Switzerland 5 <1%
Belgium 4 <1%
Brazil 4 <1%
Other 35 2%
Unknown 1429 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 430 28%
Researcher 297 19%
Student > Master 244 16%
Student > Bachelor 94 6%
Student > Doctoral Student 90 6%
Other 206 13%
Unknown 182 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 284 18%
Environmental Science 188 12%
Computer Science 170 11%
Engineering 130 8%
Earth and Planetary Sciences 76 5%
Other 414 27%
Unknown 281 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 19 October 2020.
All research outputs
#841,696
of 19,172,905 outputs
Outputs from BMC Bioinformatics
#96
of 6,530 outputs
Outputs of similar age
#3,667
of 102,134 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 19,172,905 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,530 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 98% 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 102,134 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 96% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them