Answer Posted / Pramod Kumar Saxena

In machine learning, bias refers to the error introduced by approximating a complex real-world problem with a simpler mathematical model. A high bias leads to underfitting (when the model is too simple), while a low bias results in overfitting (when the model learns noise instead of underlying patterns). The trade-off between bias and variance is essential when designing machine learning models.

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