Understanding what machine learning produces - Part I: Representations and their comprehensibility
Sally Jo Cunningham, Matthew C. Humphrey, Ian H. Witten · 1996
Abstract—The aim of many machine learning users is to comprehend the structures that are inferred from a dataset, and such users may be far more interested in understanding the structure of their data than in predicting the outcome of new test data. Part I of this paper surveys representations based on decision trees, production rules and decision graphs, that have been developed and used for machine learning. These representations have differing degrees of expressive power, and particular attention is paid to their comprehensibility for nonspecialist users. The graphic form in which a structure is portrayed also has a strong effect on comprehensibility, and Part II of this paper develops knowledge visualization techniques that are particularly appropriate to help answer the questions that machine learning users typically ask about the structures produced. The result of machine learning can be evaluated from two quite different points of view: how the knowledge that is acquired performs in new situations; and how well users comprehend the explicit descriptions of knowledge that are generated. In the literature, machine learning schemes are usually assessed on their performance alone. Techniques such as evaluation on test sets and cross-validation are specifically designed to