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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Random forest methodology for model-based recursive partitioning: the mobForest package for R
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-125 |
| Pubmed ID | |
| Authors |
Nikhil R Garge, Georgiy Bobashev, Barry Eggleston |
| Abstract |
Recursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. Model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 20% |
| Germany | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 80% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 2 | 2% |
| United States | 2 | 2% |
| Sweden | 1 | <1% |
| France | 1 | <1% |
| Russia | 1 | <1% |
| Slovenia | 1 | <1% |
| Unknown | 103 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 25 | 23% |
| Student > Ph. D. Student | 17 | 15% |
| Student > Master | 12 | 11% |
| Student > Bachelor | 10 | 9% |
| Other | 6 | 5% |
| Other | 23 | 21% |
| Unknown | 18 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 22 | 20% |
| Psychology | 16 | 14% |
| Medicine and Dentistry | 10 | 9% |
| Computer Science | 7 | 6% |
| Environmental Science | 7 | 6% |
| Other | 24 | 22% |
| Unknown | 25 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 23 April 2013.
All research outputs
#12,582,557
of 22,705,019 outputs
Outputs from BMC Bioinformatics
#3,591
of 7,254 outputs
Outputs of similar age
#99,651
of 199,477 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 136 outputs
Altmetric has tracked 22,705,019 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 199,477 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.