Answer / Shiv Dutt Dayal

Overfitting occurs when a machine learning model is too complex for the data it's trained on, causing it to learn patterns that do not generalize well to new data. This can lead to poor performance in real-world scenarios and increased risk of making incorrect decisions. Underfitting, on the other hand, happens when a model is too simple and unable to capture essential features in the training data, resulting in suboptimal predictions.

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