A great many tools have been developed for supervised classification, ranging from early
methods such as linear discriminant analysis through to modern developments such as …

Breakthroughs in machine learning are rapidly changing science and society, yet our
fundamental understanding of this technology has lagged far behind. Indeed, one of the …

This essay gives advice to authors of papers on machine learning, although much of it
carries over to other computational disciplines. The issues covered include the material that …

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 …

Predictive accuracy has been widely used as the main criterion for comparing the predictive
ability of classification systems (such as C4. 5, neural networks, and Naive Bayes). Most of …

Classifier error is the product of model bias and data variance. While understanding the bias
involved when selecting a given learning algorithm, it is similarly important to understand the …

The field of machine learning has matured to the point where many sophisticated learning
approaches can be applied to practical applications. Thus it is of critical importance that …

Predictive accuracy has been used as the main and often only evaluation criterion for the
predictive performance of classification learning algorithms. In recent years, the area under …

The selection of the best classification algorithm for a given dataset is a very widespread
problem. It is also a complex one, in the sense it requires to make several important …

Machine learning methods are now an important tool for scientists, researchers, engineers,
and students in a wide range of areas. Many years ago, one could publish papers …