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Reviewing Poor Training Examples to Identify Training Error Categories
If a system is not doing well on the training set, one option is to listen to about 100 examples that the algorithm is doing poorly on to understand major categories of training set errors. These errors can be counted in different categories.
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Reviewing Poor Training Examples to Identify Training Error Categories
When is error analysis on the training data potentially useful?
High bias means the algorithm is not fitting the training set well.
For high bias, error analysis may be performed on the _____ data.
Match each training-error-analysis concept with its source-grounded meaning.
Order the reasoning process for deciding whether to analyze training errors.
Explain why training-data error analysis is relevant when an algorithm has high bias.
What analysis should a team consider after observing poor training-set fit?
How does the source define high bias, and what analysis can help?
How should training-data error analysis relate to other high-bias techniques?
Training-data error analysis can follow a protocol similar to Eyeball dev set error analysis.
Learn After
Speech Recognition Training Error Categories
When a learning algorithm performs poorly on the training set, what is the recommended first step?
True or False: Reviewing poor training examples involves categorizing and counting errors, similar to dev set error analysis.
If a system performs poorly on the training set, consider listening to about _____ examples the algorithm is doing poorly on.
Match each error analysis concept to its correct description in the context of reviewing training examples.
Order the steps for reviewing poor training examples when the system performs poorly on the training set.
Explain why reviewing ~100 poorly-performing training examples helps diagnose training set errors.
A team's training set accuracy is low; what should they do to diagnose the issue?
Briefly describe what to do when a system is not doing well on the training set.
What is the primary goal of listening to poorly-performing training examples?
True or False: The training set error review process described requires reviewing every single training example.