| Title |
Similarity encoding for learning with dirty categorical variables
|
|---|---|
| Published in |
Machine Learning, June 2018
|
| DOI | 10.1007/s10994-018-5724-2 |
| Authors |
Patricio Cerda, Gaël Varoquaux, Balázs Kégl |
X Demographics
The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 6 | 27% |
| Portugal | 2 | 9% |
| United States | 2 | 9% |
| Switzerland | 1 | 5% |
| India | 1 | 5% |
| Poland | 1 | 5% |
| Belgium | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 7 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 73% |
| Scientists | 6 | 27% |
Mendeley readers
The data shown below were compiled from readership statistics for 236 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 236 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 41 | 17% |
| Student > Master | 36 | 15% |
| Researcher | 17 | 7% |
| Student > Doctoral Student | 15 | 6% |
| Student > Bachelor | 13 | 6% |
| Other | 39 | 17% |
| Unknown | 75 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 79 | 33% |
| Engineering | 26 | 11% |
| Business, Management and Accounting | 8 | 3% |
| Economics, Econometrics and Finance | 6 | 3% |
| Physics and Astronomy | 4 | 2% |
| Other | 31 | 13% |
| Unknown | 82 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 29 March 2024.
All research outputs
#1,775,228
of 25,593,129 outputs
Outputs from Machine Learning
#35
of 1,245 outputs
Outputs of similar age
#36,399
of 342,025 outputs
Outputs of similar age from Machine Learning
#2
of 16 outputs
Altmetric has tracked 25,593,129 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,245 research outputs from this source. They receive a mean Attention Score of 4.2. 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 342,025 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 16 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 93% of its contemporaries.