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If a model performs poorly on both training and testing data what can it indicate?

Answer and explanation

Correct answer: Underfitting

In underfitting, the model fails to learn the patterns in the training data adequately, so its performance is poor on both the training and testing data. In overfitting, training performance is usually good while testing performance is poor. Exam tip: Poor performance on both datasets suggests underfitting, whereas poor performance mainly on testing data suggests overfitting.

Related tags

UnderfittingOverfittingModel EvaluationMachine Learning

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