Parameter Tuning Insights

Discover the importance of starting with a solid default model and the impact of parameter tweaking on performance. By experimenting with different model sizes and training sample amounts, you can significantly improve accuracy. Doubling your training data often leads to a predictable decrease in error rates, providing valuable insights into the cost-effectiveness of data labeling.