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Underfitting: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning seeks to build models that can accurately predict the outcomes of unseen data based on patterns learned from training data. Unfortunately, developing an effective model is no small feat and many challenges arise along the way; one major issue being underfitting. Underfitting occurs when a model is too simple to capture underlying patterns in data. ==What is Underfitting?== Underfitting occurs when a...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
[[Underfitting]] occurs when a [[model]] hasn't fully captured the underlying patterns in [[data]]. An underfit model predicts new data poorly. Things that can cause underfitting: | |||
*Model trained for too few [[epochs]] or the [[learning rate]] is too low. | |||
*Model trained with a [[regularization rate]] that is too high. | |||
*Model has too few [[hidden layers]]. | |||
*Model is trained on the wrong [[features]]. | |||
==What is Underfitting?== | ==What is Underfitting?== | ||