Supervised Learning Algorithm Selection Questions

Covers selecting and comparing supervised machine learning algorithms for classification and regression tasks. Includes core algorithm families such as logistic regression, decision trees, random forests, gradient boosting implementations like XGBoost and LightGBM, support vector machines, and neural networks, as well as linear regression and regularization techniques including lasso, ridge, and elastic net. Candidates should be able to explain when each algorithm is appropriate based on interpretability requirements, dataset size, feature characteristics, computational constraints, training and inference latency, and robustness to noise and missing data. Also covers hyperparameter tuning, cross validation, bias variance trade offs, feature engineering impact, handling imbalanced classes, evaluation metrics for classification and regression, model calibration, and how to reason about theoretical foundations and senior level trade offs between algorithmic choices.