It's crucial to prioritize data quality over model complexity when striving for improved accuracy. Even with advanced algorithms, poorly labeled data can cap performance. Exploring transfer learning can be a strategic way to leverage existing powerful models trained on larger datasets, providing a potential pathway to better outcomes.
