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Reading High Bias from Training and Dev Error
A classifier has 16% error on the training set and 17% error on the dev set. The errors are both fairly large, but the gap between them is very small. That pattern suggests high bias and low variance, so the model is underfitting.
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Variance, Informally
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Learn After
A spam filter has 18% training error and only 19% error on the dev set. What is the best description of this model?
When a model has high bias and low variance, the training error is usually much lower than the dev error because the model is overfitting.
A model that does not match the training data well and generalizes poorly is said to have high bias and low _____.
Match each term to its meaning in a simple train/dev error example.
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A model that underfits usually has low training error and much higher dev error.
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What is the name for a model that is too simple to fit the patterns in the data well?