Answer / Pranav Kumar

Bias-variance trade-off is a fundamental problem in machine learning that refers to the balance between model complexity and fitting the training data too closely. A high bias results in underfitting, where the model fails to capture the underlying patterns of the data. On the other hand, a high variance leads to overfitting, where the model performs well on the training data but poorly on new, unseen data.

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