CENG-303/503-Wk-4A-Regression and Model Evaluation

This session provides a high-level overview of Regression and Model Evaluation, two essential topics in Data Science, Data Analytics, Statistics, and Machine Learning. The video highlights how regression models are used to examine relationships between variables, predict numerical outcomes, and support data-driven decision-making. Key concepts include simple and multiple linear regression, dependent and independent variables, regression coefficients, model assumptions, prediction errors, and the difference between model training and testing. The session also introduces model evaluation concepts such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, adjusted R-squared, overfitting, underfitting, and model generalization. This video focuses on what regression and model evaluation involve, why they are important, and how they contribute to building reliable predictive models—without presenting a detailed tutorial, coding demonstration, or hands-on lab. #DataScience #DataAnalytics #Regression #LinearRegression #ModelEvaluation #MachineLearning #PredictiveAnalytics #Statistics #R-Squared #RMSE #MAE #MSE #Overfitting #Underfitting #PredictiveModeling