Bias-Variance Tradeoff in Machine Learning

This lecture explores the Bias-Variance tradeoff in machine learning, focusing on how the risk changes with the complexity of the model class. Through a small experiment on 1D-regression, the instructor demonstrates the impact of model complexity on fitting. The lecture delves into the decomposition of error, emphasizing the balance between bias and variance. By analyzing the Bias-Variance Decomposition, the instructor highlights the importance of selecting methods that achieve low bias and variance simultaneously. The lecture concludes with discussions on noise as a lower bound on achievable error, the implications of bias and variance on model predictions, and the tradeoff between model complexity and error.