Answer / Gaurav Srivastava

In machine learning, bias and variance are measures of how well a model generalizes to new data. High bias means that the model is overly simplified and may miss important patterns in the data (underfitting), while high variance means that the model fits the training data too closely and may not perform well on new data (overfitting). The trade-off between bias and variance aims to find a model with low bias and low variance, providing a good balance between complexity and generalization.

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