Classification and Regression Fundamentals Questions

Covers the core concepts and distinctions between classification and regression in supervised learning. Classification predicts discrete categories, either binary or multi class, while regression predicts continuous numerical values. Candidates should understand how to format and encode target variables for each task, common algorithms for each family, and the theoretical foundations of representative models such as linear regression and logistic regression. For regression, know least squares estimation, coefficients interpretation, residual analysis, assumptions of the linear model, R squared, and common loss and error measures including mean squared error, root mean squared error, and mean absolute error. For classification, know logistic regression with its sigmoid transformation and probability interpretation, decision trees, k nearest neighbors, and other basic classifiers; understand loss functions such as cross entropy and evaluation metrics including accuracy, precision, recall, F one score, and area under the receiver operating characteristic curve. Also be prepared to discuss model selection, regularization techniques such as L one and L two regularization, handling class imbalance, calibration and probability outputs, feature preprocessing and encoding for targets and inputs, and trade offs when choosing approaches based on problem constraints and data characteristics.