What is bias-variance decomposition analysis? Explain the trade-off between bias and variance in model performance. Describe how bias and variance contribute to errors in predictions and how they are calculated or estimated in the context of this decomposition. Additionally, discuss the relationship between model complexity, bias, and variance, and how this analysis helps in understanding and improving model performance. Can you provide examples of strategies or techniques to address high bias or high variance in machine learning models based on insights from bias-variance decomposition?

The bias-variance trade-off is a fundamental concept that helps us understand a model's generalization error.

Bias-Variance Decomposition

Bias refers to the error introduced by approximating a real problem with a simplified model. It represents the difference between the...