The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Variable Importance Assessment in Regression: Linear Regression versus Random Forest
Abstract
Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods for decomposing R2 are among the state-of-the-art methods, although the mechanism behind their behavior is not (yet) completely understood. Random forests—a machine-learning tool for classification and regression proposed a few years ago—have an inherent procedure of producing variable importances. This article compares the two approaches (linear model on the one hand and two versions of random forests on the other hand) and finds both striking similarities and differences, some of which can be explained whereas others remain a challenge. The investigation improves understanding of the nature of variable importance in random forests. This article has supplementary material online.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas169 · Cited by 2,714
Relative Importance for Linear Regression in R : The Package relaimpo
Grömping, U. (2006) · Journal of Statistical Software · doi:10.18637/jss.v017.i01 · Cited by 2,615
Self-citation
Cited by
Variable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619
Hyperparameters and tuning strategies for random forest
Probst, P., Wright, M. N., & Boulesteix, A. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1301 · Cited by 1,640
Random Forests for Global and Regional Crop Yield Predictions
Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K.-M., Gerber, J. S., Reddy, V. R., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0156571 · Cited by 893
Population flow drives spatio-temporal distribution of COVID-19 in China
Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020) · Nature · doi:10.1038/s41586-020-2284-y · Cited by 835
Tyralis, H., Papacharalampous, G., & Langousis, A. (2019) · Water · doi:10.3390/w11050910 · Cited by 771
Metadata
- Authors
- Grömping, U.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2009-11
- Volume
- 63
- Issue
- 4
- Pages
- 308-319
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2101664201
- Cited by
- 1,103
- References
- 6
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Variable Importance Assessment in Regression: Linear Regression versus Random Forest. https://app.cochat.ai/papers/10.1198/tast.2009.08199
- The paper
- Grömping, U. (2009). Variable Importance Assessment in Regression: Linear Regression versus Random Forest. The American Statistician. https://doi.org/10.1198/tast.2009.08199
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
