Bias Variance Tradeoff and Model Selection Questions

Covers the fundamental bias and variance decomposition in supervised learning, including how model bias leads to underfitting and model variance leads to overfitting. Candidates should understand how model capacity and complexity, training data size, and noise influence bias and variance, and how these factors affect generalization error. Assessment includes diagnosing high bias versus high variance from training and validation metrics and learning curves, and applying appropriate remedies such as increasing model complexity or features for high bias, and applying regularization, early stopping, dropout, ensembling, or collecting more data for high variance. Includes knowledge of model selection and hyperparameter tuning techniques such as cross validation and validation curves, practical tradeoffs when choosing models, and how to interpret diagnostic plots and metrics to make decisions that improve real world performance.