Comprehensive Algorithm Portfolio Evaluation using Item Response Theory
Sevvandi Kandanaarachchi Kate Smith-Miles
Item Response Theory (IRT) has been proposed within the field of Educational
Psychometrics to assess student ability as well as test question difficulty and
discrimination power. More recently, IRT has been applied to evaluate machine
learning algorithm performance on a single classification dataset, where the
student is now an algorithm, and the test question is an observation to be
classified by the algorithm. In this paper we present a modified IRT-based
framework for evaluating a portfolio of algorithms across a repository of
datasets, while simultaneously eliciting a richer suite of characteristics -
such as algorithm consistency and anomalousness - that describe important
aspects of algorithm performance. These characteristics arise from a novel
inversion and reinterpretation of the traditional IRT model without requiring
additional dataset feature computations. We test this framework on algorithm
portfolios for a wide range of applications, demonstrating the broad
applicability of this method as an insightful algorithm evaluation tool.
Furthermore, the explainable nature of IRT parameters yield an increased
understanding of algorithm portfolios.