Abstract
This paper describes some results from the ESPRIT project StatLog whose purpose is to compare the performance of a wide variety of learning algorithms on a wide variety of datasets. The algorithms come from the areas of statistics, machine learning, neural networks, genetic algorithms and relational learning algorithms. In the whole project, there are about 20 datasets, coming from such areas as medicine, finance, image analysis, protein structure analysis and engineering design. This paper describes the results from just two classification problems and one prediction problem. In these cases, the most successful algorithms come from statistics, and the reasons for the relative failure of machine learning and neural net procedures are discussed.
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