The DOI matches its OpenAlex record; 21 of the 21 references it cites were found in a registry or an index.
Understanding Random Forests: From Theory to Practice
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Cited by
Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W., & Zhou, H. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.085 · Cited by 560
Machine learning technology in biodiesel research: A review
Aghbashlo, M., Peng, W., Tabatabaei, M., Kalogirou, S. A., Soltanian, S., Hosseinzadeh-Bandbafha, H., Mahian, O., & Lam, S. S. (2021) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2021.100904 · Cited by 426
Robust EEG-based cross-site and cross-protocol classification of states of consciousness
Engemann, D. A., Raimondo, F., King, J.-R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez-Slezak, D., et al. (2018) · Brain · doi:10.1093/brain/awy251 · Cited by 329
Self-citationArtificial intelligence explainability: the technical and ethical dimensions
McDermid, J. A., Jia, Y., Porter, Z., & Habli, I. (2021) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2020.0363 · Cited by 136
Jaxa-Rozen, M., & Kwakkel, J. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.06.011 · Cited by 91
Metadata
- Authors
- Louppe, G.
- Venue
- arXiv (Cornell University)
- Publisher
- Cornell University
- Published
- 2014-01-01
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W2124833832
- Cited by
- 563
- References
- 21
- Type
- posted content
- Language
- en
- Open access
- green
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- Louppe, G. (2014). Understanding Random Forests: From Theory to Practice. arXiv (Cornell University). https://doi.org/10.13140/2.1.1570.5928
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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.
