Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation. (arXiv:2112.11832v2 [cs.LG] UPDATED)

Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs), or
systems that contain ML models, is highly challenging. In addition to the
challenges of testing classical software, it is acceptable and expected that
statistical ML models sometimes output incorrect results. A major challenge is
to determine when the level of incorrectness, e.g., model accuracy or F1 score
for classifiers, is acceptable and when it is not. In addition to business
requirements that should provide a threshold, it is a best …

arxiv classifier complexity data data quality geometric complexity insights quality