Answer / Jay Kumar Gaur

The bias-variance tradeoff is a dilemma in supervised learning where we aim to balance between a model's ability to make accurate predictions (low bias) and its sensitivity to the training data (low variance). A low bias model may underfit the data, while a low variance model may overfit the data. Finding the right balance is crucial for good generalization performance.

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