Bias-Variance Trade-off

This lecture delves into the bias-variance trade-off in machine learning, exploring how model complexity impacts prediction quality. It explains how bias measures prediction accuracy, variance assesses prediction consistency, and noise sets a lower bound on error. By finding the right balance between bias and variance, a model can achieve optimal prediction performance. The instructor illustrates this concept through a detailed analysis of the bias, variance, and noise components, showing how they interact to determine the overall prediction error. The lecture concludes by emphasizing the importance of selecting a model complexity that minimizes both bias and variance to achieve accurate and consistent predictions.