Answer / Komal Rani

Bias refers to a situation where a model systematically underestimates or overestimates the actual outcomes. High bias means the model is too simple, missing important aspects of the data, while low bias means it's too complex and captures noise. Variance, on the other hand, measures how much a model's predictions change based on different training samples. Low variance means the model is robust, but high variance indicates that the model is unstable.

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