A comparative study of several classification metrics and their performances on data
Department of Statistics, Chukwuemeka Odumegwu Ojukwu University, Anambra State, Nigeria.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 08(01), 308–314.
Article DOI: 10.30574/wjaets.2023.8.1.0054
Publication history:
Received on 06 January 2023; revised on 14 February 2023; accepted on 17 February 2023
Abstract:
Six classification metrics namely, Accuracy, Precision, Recall (Sensitivity), Specificity, F1-Score and Area Under the Curve have been studied in this work. A classification model based on the Support Vector Machine, was used to obtain a confusion matrix, which provided the needed information for calculating the different classification metrics. Twenty different datasets were used to assess the performances of the classification metrics. Accuracy and Area Under the Curve are the two metrics that consistently gave a classification result given each dataset used in the study. Although accuracy appears to be marginally better that AUC, it was discovered that in some cases where sensitivity is zero, accuracy yielded a high correct classification result. This goes further to implying that prior to choosing accuracy as a preferred metric for classification, investigation should be carried out to find out what sensitivity and specificity are. Where there are high values for sensitivity and specificity, the study shows that a choice of accuracy as a preferred classification metric leads to a high percentage of correct classification result.
Keywords:
Classification Metrics; Machine Learning; Confusion Matrix; Support Vector Machines
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0