Extremely randomized trees
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Non-linear models encompassing non-parametric, kernel-based and tree-ensemble approaches enable a comprehensive comparison.
“These non-linear models encompass non-parametric (KNN), kernel-based (KRR, SVR and GP) and tree-ensemble (RFR, XGB and ETR) approaches, enabling a comprehensive comparison34,35,47.”
From Data-driven de novo design of super-adhesive hydrogels (2025) · cites this paper as [34]
The ET Regressor performed best among the compared regression models for predicting TAT_pipet in terms of MAE, R2, and MAPE.
“Out of the regression models in PyCaret, the ET Regressor [16] performed best in terms of MAE, R 2 and MAPE (Table 3).”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
The ET Regressor is computationally efficient.
“The ET Regressor is also computationally efficient [16].”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
To minimize bias, the ET Regressor uses the full original training set instead of bootstrap replicas.
“At the same time, to minimize bias, the ET Regressor uses the full original training set instead of bootstrap replicas [16].”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
The high level of randomization of the ET Regressor drastically reduces variance and thus the probability of overfitting.
“Furthermore, the high level of randomization of the ET Regressor drastically reduces the variance [16], and thus the probability of overfitting.”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
The smaller F (number of features evaluated at a node), the stronger the randomization of the trees and the weaker the dependence of the structure of the trees on TAT values of the training set samples.
“The smaller F (number of features evaluated at a node), the stronger the randomization of the trees and the weaker the dependence of the structure of the trees on TAT values of the training set samples [16].”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
The noisier the output, the higher the optimal value of n_min (minimum number of samples for splitting a node) to minimize overfitting by creating smaller trees.
“The noisier the output, the higher the optimal value of n min (minimum number of samples for splitting a node) to minimize overfitting by creating smaller trees [16].”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
The best choice for T (number of generated trees) is a tradeoff between computation time and accuracy.
“The best choice for T (number of generated trees) is a tradeoff between computation time and accuracy, i.e., the more trees generated, the better the accuracy but the higher the computation time [16].”
From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [16]
Research efforts generated some of the most widely used implementations of ensemble DT as of today: MART, extra-trees, and XGBoost.
“Research efforts in the field generated some of the the most widely used implementations of ensemble DT as of today: Multiple Additive Regression Trees (MART) [154], extra-trees [164], and eXtreme Gradient Boosting (XGBoost) [93].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [164]
The Extra-Tree algorithm of Geurts et al. (2006) has similar accuracy performance while being more computationally efficient.
“This algorithm has similar accuracy performance while being more computationally efficient.”
From A random forest guided tour (2016) · cites this paper as Geurts et al. (2006)
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Refinement of protein-protein complexes in contact map space with metadynamics simulations
Pfeiffenberger, E., & Bates, P. A. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25612 · Cited by 15 · see citing works
Machine-learnt versus analytical models of TCP throughput
El Khayat, I., Geurts, P., & Leduc, G. (2007) · Computer Networks · doi:10.1016/j.comnet.2006.11.017 · Cited by 10 · see citing works
Self-citationAlmalawi, A. (2025) · Sensors · doi:10.3390/s25072235 · Cited by 10 · see citing works
Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson’s disease
Loo, R. T. J., Pavelka, L., Mangone, G., Khoury, F., Vidailhet, M., Corvol, J.-C., Glaab, E., On behalf of the NCER-PD Consortium, Acharya, G., Aguayo, G., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01862-1 · Cited by 9 · see citing works
“Machine learning algorithms for predicting the graduation admission,”
Mridha, K., Jha, S., Shah, B., Damodharan, P., Ghosh, A., & Shaw, R. N. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-1677-9_55 · Cited by 3 · see citing works
Adama, S., & Bogdan, M. (2026) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2026.1797090 · Cited by 1 · see citing works
Sonuç, E., Saihood, Q., Yargı Baydilli, Y., & Özcan, E. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871689
“Three ensemble strategies were employed (39): (1) Bagging : Random Forest (RF) (40) and Extremely Randomized Trees (ET) (41); (2) Boosting : Extreme Gradient Boosting (XGB) (42) and Light Gradient Boosting Machine (LGBM) (43); (3) Stacking : All possible combinations of the four individual…”
Parsimonious statistical learning models for low flow estimation
Laimighofer, J., Melcher, M., & Laaha, G. (2021) · doi:10.5194/hess-2021-481 · Cited by 30 · see citing works
“We used the estimated response variance as a splitting criterion because rank-based approaches or the use of extremely randomized trees (Geurts et al., 2006) did not improve prediction accuracy.”
Method
- Authors
- Geurts, P., Ernst, D., & Wehenkel, L.
- Affiliations
- University of Liège (BE)
- Corresponding author
- Geurts, Pierre
- Venue
- Machine Learning
- Published
- 2006-03-02
- Volume
- 63
- Issue
- 1
- Pages
- 3-42
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2056132907
- Cited by
- 9,300
- References
- 50
- Citations by year
- 2026: 1390 · 2025: 1329 · 2024: 1203 · 2023: 1037 · 2022: 870 · 2021: 877 · 2020: 645 · 2019: 497 · 2018: 342 · 2017: 256 · 2016: 208 · 2015: 141 · 2014: 126 · 2013: 85 · 2012: 61
- FWCI
- 45.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Open Repository and Bibliography (University of Liège) (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- Fonds De La Recherche Scientifique - FNRS
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- extra trees, tree-based ensemble methods, supervised classification, regression analysis, computational efficiency, bias-variance analysis, kernel characterization, geometric characterization, parameter tuning
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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.
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