Evaluation of Predictive Capabilities of Similarity Metrics in Machine Learning

Abstract

This paper explores prediction capabilities of similarity metrics used in machine learning algorithms. Predictive capabilities of various similarity metrics are examined based on their application on data sets of varying sizes and properties and evaluation of derived results. Predicting outcomes in machine learning is fundamental to many different machine learning algorithms and the findings in this paper will clarify how good their predictive capabilities are and under which conditions.