Underfitting
occurs when a model is too simple to capture the underlying structure of the data, resulting in poor performance on both the and unseen . This phenomenon typically arises when the model suffers from high , meaning it makes strong, incorrect assumptions about the data distribution, such as applying a to a non-linear relationship. Common causes include insufficient , excessive , or an inadequate number of during the training process. Unlike , where a model memorizes , an underfit model fails to learn the essential and patterns, necessitating a transition to more sophisticated like or expanding the .
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