ROC curve, or receiver operating characteristic curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system. ROC space is defined by FPR (false positive rate) and TPR (true positive rate) as x and y axes, respectively, which depicts a relative trade-off between truly predicted and falsely predicted classes. The total area is bounded by the unit square [0 to 1] on x-axis, and [0 to 1] on y-axis. AUC area extends from the 45°-line (that connects (0,0) and (1,1)) towards the top left (0,1) covering 100% total area. A higher AUC area closer to (0,1) depicts a stronger classification method.
Published in Chapter:
Employee Classification in Reward Allocation Using ML Algorithms
Parakramaweera Sunil Dharmapala (Lone Star College, Cypress, USA)
Copyright: © 2023
|Pages: 18
DOI: 10.4018/978-1-7998-9220-5.ch186
Abstract
This work discussed an application of machine learning algorithms in predicting employee categories in reward allocation based on input features determined from survey responses. The results reported in this article are primarily based on beliefs and perceptions of the survey respondents about the four categories of employees, namely performer, needy, starter, and senior. The authors considered two classification models—full model with 10 input features and the reduced model with seven input features—and the results show that the reduced model performed better than the full model, indicating that three qualitative input features bear no relevance to predicting the employee categories. Both models selected optimizable ensemble and optimizable SVM as best machine learning classifiers, based on accuracy rates and AUC scores. Finally, using the reduced model on out-of-sample observations, employee categories were correctly predicted matching the actual categories.