The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Bias-corrected random forests in regression
Abstract
It is well known that random forests reduce the variance of the regression predictors compared to a single tree, while leaving the bias unchanged. In many situations, the dominating component in the risk turns out to be the squared bias, which leads to the necessity of bias correction. In this paper, random forests are used to estimate the regression function. Five different methods for estimating bias are proposed and discussed. Simulated and real data are used to study the performance of these methods. Our proposed methods are significantly effective in reducing bias in regression context.
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Lower observed travel rates tended to be overpredicted by the model, while higher rates were consistently underpredicted.
“Lower observed travel rates tended to be overpredicted by the model, while higher rates were consistently underpredicted as is common with random forests (Zhang and Lu 2011).”
From Machine learning estimation of off-trail pedestrian travel rates using LiDAR-derived slope, vegetation, and surface roughness (2026) · cites this paper as Zhang and Lu 2011
Citations
Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
Random Forests and Adaptive Nearest Neighbors
Lin, Y., & Jeon, Y. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001230 · Cited by 507
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Cutler, S. L., Campbell, M. J., Brewer, S. C., & Dennison, P. E. (2026) · GIScience & Remote Sensing · doi:10.1080/15481603.2026.2626632
Metadata
- Authors
- Zhang, G., & Lu, Y.
- Venue
- Journal of Applied Statistics
- Publisher
- Informa UK Limited
- Published
- 2012-01
- Volume
- 39
- Issue
- 1
- Pages
- 151-160
- ISSN
- 0266-4763
- 1360-0532
- Cited by
- 196
- References
- 3
- Language
- en
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