Is model accuracy important or model performance?

This question is ideal for testing the fluency of an individual regarding machine learning model performance. Models with higher accuracy could not perform well in terms of predictive power. How does this happen? Generally, the model accuracy is a subset of model performance, and it can also be misleading at certain times. If you have to detect fraud in large datasets with a sample of millions, the more accurate model would not predict any fraud.

This condition is possible if only a large minority of cases, involving fraud. As for a predictive model, this condition would be inappropriate. Imagine that a model designed for fraud shows that there is no sign of fraud! Therefore, we can clearly ascertain that model accuracy is not the sole determinant of model performance. Both elements are highly significant in machine learning.