Decision trees are intelligible, but do they perform well enough that you should use them? Have SVMs replaced neural nets, or are neural nets still best for regression, and SVMs best for classification? Boosting maximizes margins similar to SVMs, but can boosting compete with SVMs? And if it does compete, is it better to boost weak models, as theory might suggest, or to boost stronger models? Bagging is simpler than boosting -- how well does bagging stack up against boosting? Breiman said Random Forests are better than bagging and as good as boosting. Was he right? And what about old friends like logistic regression, KNN, and naive bayes? Should they be relegated to the history books, or do they still fill important niches?

In this talk we compare the performance of ten supervised learning methods on nine criteria: Accuracy, F-score, Lift, Precision/Recall Break-Even Point, Area under the ROC, Average Precision, Squared Error, Cross-Entropy, and Probability Calibration. The results show that no one learning method does it all, but some methods can be "repaired" so that they do very well across all performance metrics. In particular, we show how to obtain the best probabilities from max margin methods such as SVMs and boosting via Platt's Method and isotonic regression. We then describe a new ensemble method that combines select models from these ten learning methods to yield much better performance. Although these ensembles perform extremely well, they are too complex for many applications. We'll describe what we're doing to try to fix that. Finally, if time permits, we'll discuss how the nine performance metrics relate to each other, and which of them you probably should (or shouldn't) use.

During this talk I'll briefly describe the learning methods and performance metrics to help make the lecture accessible to non-specialists in machine learning.