An Empirical Comparison of Supervised Learning Algorithms
Abstract
A number of supervised learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of supervised learning was the Statlog Project in the early 90's. We present a large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the learning methods.
Cite
Text
Caruana and Niculescu-Mizil. "An Empirical Comparison of Supervised Learning Algorithms." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143865Markdown
[Caruana and Niculescu-Mizil. "An Empirical Comparison of Supervised Learning Algorithms." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/caruana2006icml-empirical/) doi:10.1145/1143844.1143865BibTeX
@inproceedings{caruana2006icml-empirical,
title = {{An Empirical Comparison of Supervised Learning Algorithms}},
author = {Caruana, Rich and Niculescu-Mizil, Alexandru},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {161-168},
doi = {10.1145/1143844.1143865},
url = {https://mlanthology.org/icml/2006/caruana2006icml-empirical/}
}