What do you mean by overfitting and underfitting algorithms?
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Answer / Ruchi
Overfitting occurs when a model learns the training data too well, including its noise and outliers, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, leading to poor performance even on the training data.
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