Assembling Benchmark Datasets for Machine Learning Classification

Authors

Parker Hollingsworth, Brigham Young University
Stephen Piccolo, Brigham Young University

Keywords

benchmark datasets, machine learning classification, algorithms

College

Life Sciences

Department

Biology

Abstract

Machine learning classification is a type of artificial intelligence that learns from data and makes predictions. There are many different algorithms that can be used to develop predictive models for machine learning. Generally the algorithm looks for patterns in the data and uses those patterns to make predictions on an additional data set. This type of artificial intelligence is being used increasingly in the biomedical community to predict disease diagnosis and prognosis. Although machine learning has shown to provide promising results, it is far from perfect. The accuracy of the predictive model often depends on arbitrary decisions made by researchers. Researchers are often unsure which algorithms or which features of the data to use in the analysis. In this paper, I apply classification algorithms to five datasets and compare the accuracy across 24 different algorithms.

Recommended Citation

Hollingsworth, Parker and Piccolo, Stephen (2016) "Assembling Benchmark Datasets for Machine Learning Classification," Journal of Undergraduate Research: Vol. 2016: Iss. 1, Article 149.
Available at: https://scholarsarchive.byu.edu/jur/vol2016/iss1/149