In which real-world scenarios do overfitting and underfitting tend to occur? How do these occurrences affect the accuracy and performance of the model?

Question

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.

In which real-world scenarios do overfitting and underfitting tend to occur? How do these occurrences affect the accuracy and performance of the model?

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