Lecture
Bias-Variance Trade-Off
Description
This lecture covers the concepts of underfitting and overfitting in machine learning models, explaining how hyperparameters control model flexibility. It delves into error decomposition, polynomial regression, and K Nearest-Neighbors, illustrating the bias-variance trade-off. The instructor discusses the impact of model flexibility on training and test errors, emphasizing the importance of finding the right balance to avoid underfitting or overfitting.
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