The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
An empirical evaluation of supervised learning in high dimensions
Abstract
In this paper we perform an empirical evaluation of supervised learning on high-dimensional data. We evaluate perfor-mance on three metrics: accuracy, AUC, and squared loss and study the eect of increas-ing dimensionality on the performance of the learning algorithms. Our ndings are con-sistent with previous studies for problems of relatively low dimension, but suggest that as dimensionality increases the relative perfor-mance of the learning algorithms changes. To our surprise, the method that performs consistently well across all dimensions is ran-dom forests, followed by neural nets, boosted trees, and SVMs. 1.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280
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Metadata
- Authors
- Caruana, R., Karampatziakis, N., & Yessenalina, A.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 96-103
- OpenAlex
- W2128302979
- Cited by
- 524
- References
- 2
- FWCI
- 9.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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