what is the difference between overfitting and underfitting?

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Accepted Answer

Overfitting occurs when a model learns the training data too well, leading to poor generalization. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

Overfitting: model performs well on training data but poorly on unseen data
Underfitting: model is too simple and fails to capture the underlying patterns in the data
Overfitting can be addressed by using techniques like regularization, cross-validation, and early stopping
Underfitting can be addressed by using more complex models, increasing model complexity, or adding more features
Example: A model that perfectly predicts all training data points but fails to generalize to new data is overfitting

what is the difference between overfitting and underfitting?

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