Explain the bias-variance tradeoff.

The bias-variance tradeoff refers to the balance between a model’s complexity and its ability to generalize:
Bias is the error introduced by overly simplistic models that miss important patterns in the data (leading to underfitting).
Variance is the error introduced by models that are too complex and overly sensitive to small fluctuations in the training data (leading to overfitting). The goal is to find a balance where both bias and variance are minimized.