Data Over Model

Increasing model complexity or tweaking algorithms won't always lead to better performance if the underlying data is flawed. A case study revealed that mislabeled data can cap model accuracy, emphasizing the importance of data integrity. Exploring transfer learning can also provide a pathway to leverage more robust models trained on larger datasets, potentially overcoming data limitations.