How does the bias-variance trade-off affect model performance?

The bias-variance trade-off affects model performance by balancing two sources of error:

1. Bias: Error due to overly simplistic models that underfit the data, leading to high training and test errors.
2. Variance: Error due to overly complex models that overfit the data, leading to low training error but high test error.
Impact on Performance:
* High Bias: Leads to underfitting and poor model performance on both training and test data.
* High Variance: Leads to overfitting and excellent training performance but poor generalization to new data.