According to the error comparison table, what primary issue characterizes a model where both training error and test error are high?
Answer
Underfitting, because the model is too simple to capture the essential relationships present in the data.
When both errors are high, the model has failed to learn the basic signal, whether due to low capacity or over-constraint, leading to poor performance across the board.
Related Questions
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