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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Generalising Random Forest Parameter Optimisation to Include Stability and Cost
|
|---|---|
| Chapter number | 9 |
| Book title |
Machine Learning and Knowledge Discovery in Databases
|
| Published in |
arXiv, September 2017
|
| DOI | 10.1007/978-3-319-71273-4_9 |
| Book ISBNs |
978-3-31-971272-7, 978-3-31-971273-4
|
| Authors |
C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Angelo Cardoso, Ângelo Cardoso |
X Demographics
The data shown below were collected from the profiles of 33 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 21% |
| United Kingdom | 6 | 18% |
| Spain | 4 | 12% |
| China | 1 | 3% |
| Chile | 1 | 3% |
| Canada | 1 | 3% |
| Rwanda | 1 | 3% |
| Netherlands | 1 | 3% |
| Sweden | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 10 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 26 | 79% |
| Scientists | 6 | 18% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 11% |
| Researcher | 4 | 9% |
| Student > Doctoral Student | 4 | 9% |
| Student > Bachelor | 3 | 7% |
| Other | 3 | 7% |
| Other | 8 | 18% |
| Unknown | 18 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 31% |
| Medicine and Dentistry | 3 | 7% |
| Engineering | 2 | 4% |
| Mathematics | 1 | 2% |
| Agricultural and Biological Sciences | 1 | 2% |
| Other | 5 | 11% |
| Unknown | 19 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 26. 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 16 March 2020.
All research outputs
#1,448,409
of 24,985,232 outputs
Outputs from arXiv
#22,357
of 1,018,505 outputs
Outputs of similar age
#28,556
of 323,874 outputs
Outputs of similar age from arXiv
#412
of 18,313 outputs
Altmetric has tracked 24,985,232 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,018,505 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done particularly well, scoring higher than 97% 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 323,874 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 91% of its contemporaries.
We're also able to compare this research output to 18,313 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 97% of its contemporaries.