What should you do when training performance is poor?
Question: In one to three sentences, explain the recommended next step when a model is not fitting the training data well.
Sample answer: Inspect about 100 examples the model handles badly, group the mistakes into a few categories, and count how often each category appears.
Key points:
- Review roughly 100 weak training examples
- Classify and tally the error types
Rubric: Full credit for stating that about 100 bad examples should be reviewed and that the errors should be grouped and counted by category.
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What should you do first when a model is making many mistakes on the training set?
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How many bad training examples should you inspect first?
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Put the training-set review process in the correct order.
Why inspecting a small set of bad training cases can reveal training-set problems
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What should you do when training performance is poor?
What is the main purpose of reviewing training examples that the model handles poorly?
True or False: Diagnosing model failures requires checking every training example in the dataset.