An Empirical Comparison of Supervised Learning Algorithms Using Different Performance Metrics
We present results from a large-scale empirical comparison between ten learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We evaluate the methods on binary classification problems using nine performance criteria: accuracy, squared error, cross-entropy, ROC Area, F-score, precision/recall breakeven point, average precision, lift, and calibration. Because some models (e.g. SVMs and boosted trees) do not predict well-calibrated probabilities, we compare the performance of the algorithms both before and after calibrating their predictions with Platt Scaling and Isotonic Regression. Before scaling, the models with the best overall performance are neural nets, bagged trees, and random forests. After scaling, the best models are boosted trees, random forests, and unscaled neural nets.