Answer Posted / Saroj Kumar

Bias and variance are two sources of error in machine learning models. Bias refers to the amount that a model underestimates or overestimates the true relationship between input and output variables. Variance refers to how much the model's predictions change based on small changes in the training data.nA high bias leads to underfitting, where the model is too simple and unable to capture the complexities of the data, while a high variance leads to overfitting, where the model captures noise in the training data rather than the underlying pattern.

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