Predictive performance evaluation is a fundamental issue in design, development, and
deployment of classification systems. As predictive performance evaluation is a …

Abstract Machine learning is a powerful tool for gleaning knowledge from massive amounts
of data. While a great deal of machine learning research has focused on improving the …

The emphasis of the book is on the question of Why–only if why an algorithm is successful is
understood, can it be properly applied, and the results trusted. Algorithms are often taught …

There are several reasons to evaluate a multi-class classifier on other measures than just
error rate. Perhaps most importantly, there can be uncertainty about the exact context of …

Probabilistic classifiers are developed by assuming generative models which are product
distributions over the original attribute space (as in naive Bayes) or more involved spaces …

Learning curves are a concept from social sciences that has been adopted in the context of
machine learning to assess the performance of a learning algorithm with respect to a certain …

The area under the ROC curve (AUC) is a very widely used measure of performance for
classification and diagnostic rules. It has the appealing property of being objective, requiring …

An assumption fundamental to almost all work on supervised classification is that the
probabilities of class membership, conditional on the feature vectors, are stationary …

The bias-variance decomposition is a very useful and widely-used tool for understanding
machine-learning algorithms. It was originally developed for squared loss. In recent years …

Machine learning has come of age. And just in case you might think this is a mere platitude,
let me clarify. The dream that machines would one day be able to learn is as old as …