Overfitting, Underfitting, and Model Validation Questions

Covers the concepts of overfitting and underfitting in predictive models and the validation techniques used to detect and prevent them. Candidates should understand the bias variance tradeoff and how model complexity, data quality, and training procedures influence underfitting and overfitting. Topics include train, validation, and test splits, k fold cross validation and other resampling strategies, and how to interpret learning curves to diagnose problems. Assessment and metrics for classification and regression should be known and used to compare models on held out data. Mitigation strategies include regularization techniques, model simplification, feature engineering, obtaining more or better data, data augmentation, early stopping, and ensemble methods. Candidates should also be familiar with hyperparameter tuning workflows, validation pitfalls such as data leakage, and practical diagnostics to decide whether to increase model capacity or increase regularization.