Which ONE of the following is MOST likely to indicate a problem with underfitting in a machine learning model?
Underfitting happens when a model is too simplistic to learn the true relationships in the data, leading to high bias and poor performance across the board — including on data that closely resembles the training set. This is the defining characteristic that distinguishes underfitting from overfitting: an overfit model performs well on training data but poorly on new/unseen data (high variance), whereas an underfit model performs poorly even on data similar to what it was trained on, since it failed to capture the underlying patterns at all.
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