Comparisons of Supervised Artificial Neural Networks With Population-Based Statistical Probability Models in Moderate Sized Samples

M. Brimacombe

International Journal of Statistics and Probability, 2025, vol. 14, issue 1, 58

Abstract: Some of the basic issues affecting the comparison of population based statistical models and data-centric artificial neural network machine learning models are reviewed in moderate sized data samples. Comparisons of artificial neural networks and population-based models should consider and reflect both the data-centric and probability based nature of the models being compared. This is examined in a series of examples. Some guidelines for developing useful comparative settings are given. Improving the understandability of machine learning methods is an important goal.

Date: 2025
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