Answer Posted / Hammad Izhar

Bias refers to the error introduced by approximating a real-world problem with a simplified model. High bias means that the model is too simple, causing underfitting. Variance, on the other hand, measures how sensitive the model's predictions are to small changes in the training data. High variance indicates that the model is overfitting.

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