Sciweavers

ICML
2008
IEEE

An empirical evaluation of supervised learning in high dimensions

14 years 11 months ago
An empirical evaluation of supervised learning in high dimensions
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and study the effect of increasing dimensionality on the performance of the learning algorithms. Our findings are consistent with previous studies for problems of relatively low dimension, but suggest that as dimensionality increases the relative performance of the learning algorithms changes. To our surprise, the method that performs consistently well across all dimensions is random forests, followed by neural nets, boosted trees, and SVMs.
Rich Caruana, Nikolaos Karampatziakis, Ainur Yesse
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Rich Caruana, Nikolaos Karampatziakis, Ainur Yessenalina
Comments (0)