(ML Spec)
error analysis is the process of manually looking through a random sample of incorrectly predicted examples from cross-validation set and try to find out their commonalities (properties, themes, etc)
- Some categories may overlap
- Focus on category with most examples for improvements, for example
- collect more data on this category
- create more features related to this category
Caveats
- error analysis is harder for tasks that humans are NOT good at
- focus on the more promising things to try