Файл:An Empirical Comparison of Supervised Learning Algorithms Caruana.icml06.pdf

Rich Caruana caruana@cs.cornell.edu Alexandru Niculescu-Mizil alexn@cs.cornell.edu Department of Computer Science, Cornell University, Ithaca, NY 14853 USA

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.

Keywords: SVM, ANN, Logistic Regression (LOGREG), Naive Bayes (NB), KNN, Random Forests (RF), Decision trees (DT), Bagged trees (BAG-DT), boosted trees (BST-DT), boosted stumps (BST-STMP)