How can overfitting and underfitting be detected through training and testing data?
- Overfitting detected by high training error; Underfitting by low testing error
- Overfitting detected by low complexity; Underfitting by high complexity
- Overfitting detected by low training error and high testing error; Underfitting by high training and testing errors
- Underfitting detected by low training error; Overfitting by low testing error
Overfitting is detected when there is low training error but high testing error, as the model fits the training data too well but fails to generalize. Underfitting is detected when both training and testing errors are high, indicating that the model fails to capture underlying trends.
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