Learn Before
High Training Error Calls for Better Fit, Not More Data
If a model already has high avoidable bias, adding more training examples usually will not solve the problem. Extra data mainly helps reduce variance, while bias changes little. The first priority is to improve performance on the training set; only after that is the model likely to show meaningful gains on dev and test sets.
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High Training Error Calls for Better Fit, Not More Data
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Learn After
An email-spam classifier has 10% training error and 11% development error, but the team wants to reach 3% error. What should they do first?
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Your model’s training error is 18%, and your goal is 6%. What should you address first?
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