Provide examples of real-world scenarios where overfitting and underfitting might occur, and explain the consequences of each for model performance and accuracy

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Anonymous · Accepted Answer

Underfitting is when a model is too simplistic in nature that it tends to
overgeneralize and does not understand complex behavioural patterns.

Overfitting is when the model is trained on too much data, that it finds
patterns in irregularities and noise.

Provide examples of real-world scenarios where overfitting and underfitting might occur, and explain the consequences of each for model performance and accuracy

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