Title |
Random generalized linear model: a highly accurate and interpretable ensemble predictor
|
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Published in |
BMC Bioinformatics, January 2013
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DOI | 10.1186/1471-2105-14-5 |
Pubmed ID | |
Authors |
Lin Song, Peter Langfelder, Steve Horvath |
Abstract |
Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward feature selection tends to overfit the data and leads to low predictive accuracy. Therefore, it remains an important research goal to combine the advantages of ensemble predictors (high accuracy) with the advantages of forward regression modeling (interpretability). To address this goal several articles have explored GLM based ensemble predictors. Since limited evaluations suggested that these ensemble predictors were less accurate than alternative predictors, they have found little attention in the literature. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 5 | 56% |
Sweden | 1 | 11% |
Unknown | 3 | 33% |
Demographic breakdown
Type | Count | As % |
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Scientists | 5 | 56% |
Members of the public | 4 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 11 | 5% |
Sweden | 3 | 1% |
Poland | 2 | <1% |
Malaysia | 1 | <1% |
New Zealand | 1 | <1% |
Italy | 1 | <1% |
Spain | 1 | <1% |
Germany | 1 | <1% |
Unknown | 203 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 53 | 24% |
Student > Ph. D. Student | 52 | 23% |
Student > Master | 31 | 14% |
Student > Postgraduate | 13 | 6% |
Professor > Associate Professor | 13 | 6% |
Other | 33 | 15% |
Unknown | 29 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 61 | 27% |
Computer Science | 48 | 21% |
Biochemistry, Genetics and Molecular Biology | 15 | 7% |
Medicine and Dentistry | 11 | 5% |
Engineering | 10 | 4% |
Other | 44 | 20% |
Unknown | 35 | 16% |