Comparing Two Eval Runs by Their Average Pass Rate Is the Wrong Test

1. 1Run multiple versions of your model against the same eval set to collect pass rate data
2. 2Calculate the average pass rate for each model version
3. 3Apply statistical tests, such as the t-test or ANOVA, to compare the pass rates of different model versions
4. 4Consider using alternative metrics, like precision, recall, or F1 score, to get a more comprehensive understanding of model performance
5. 5Visualize the pass rate distributions for each model version to identify potential outliers or biases

Data scientists and machine learning engineers can benefit from understanding the limitations of average pass rate comparisons to improve their model evaluation and selection processes.

💡 Comparing average pass rates can be misleading due to the potential for outliers, biases, and statistical fluctuations

Don't compare eval runs by average pass rate alone! Learn why and how to do it correctly #MachineLearning #ModelEvaluation