Model performance analysis goes beyond simple metrics and focuses on model robustness. Model debugging is a discipline that aims to improve model robustness.
Tip
Robustness refers to the consistency of a model’s accuracy when features change.
Measuring Model Robustness
Measuring robustness requires separate data sets for training, validation, and testing.
- Use unseen test data or generate new types of data to evaluate model robustness.
- Use the same metrics as during training, such as RMSE for regression or AUC for classification.
Techniques for Model Debugging
- Benchmarking Models: Compare your model’s performance against other models or baselines to identify areas for improvement.
- Sensitivity Analysis: Analyze how changes in input features impact model predictions to understand its behavior and identify potential weaknesses.
- Residual Analysis: Examine the discrepancies between predicted and actual values (residuals) to detect patterns or outliers that indicate model deficiencies.
These techniques form the foundation of model debugging and can help uncover and fix problems in ML models, improving their transparency, fairness, security, and overall robustness.