What are some cool error analysis tricks you've seen?

I have one. On a text classification problem we would dump prediction errors to a spreadsheet, look at them, and try to categorize the types of errors. Sometimes this is easy, like parsing errors from OCR confusing the model on a particular word. Other times it's harder because we had to understand what the model was missing.

A colleague suggested we run LDA to get general topics across the error samples to try and discern. Seemed like a really cute …

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