This lecture introduces simple validation, cross-validation, and leave-one-out CV techniques for obtaining unbiased risk estimates of learned predictors, along with their application for hyperparameter tuning.
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Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.
