Addressing Accuracy Paradox Using Enhanched Weighted Performance Metric in Machine Learning

Abstract

Accuracy metric has become a gold standard for measuring various models and systems. However, the latest development and research have shown its limitations, especially during training/testing of the machine learning models. It fails to keep the integrity of the reliability of the outcome for unbalanced data modeling. This problem is known as Accuracy Paradox. This paper presents a unique approach utilizing the underlying standard metrics and creating internals 3D evaluation of fitness factors, a part of enhanced machine learning engine engineering. The model is trained under the internal tuning of parameters and trade-off is handled as a collateral benefit using built-in parallelism. Moreover, the way this metric is engineered, it opens up generalized options for data engineers and model creators to incorporate (mathematically) the metrics (standard or the new one) of their choice based on how the model is trained for any type of data domain. This approach shows the improved integrity of the internal metrics measurements using mathematical constructs and governing algorithm for enhanced Weighted Performance Metric. Included sample experiments on real-world datasets, and results support the contribution of this research.