A Data-Driven Method for Selecting Optimal Models Based on Graphical Visualisation of Differences in Sequentially Fitted ROC Model Parameters

• Main Authors: K S Mwitondi, R E Moustafa, A S Hadi
• Format: Article
• Language: English
• Published: Ubiquity Press 2013-05-01
• Series: Data Science Journal
• Subjects: Bayesian error; Data mining; Data visualisation; Decision trees; Domain partitioning; Optimal bandwidth; ROC curves; Visual analytics; Youden Index
• Online Access: http://datascience.codata.org/articles/172

Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance.