Lecture 5: Regression Evaluation Metrics | MAE, MSE, RMSE & R² Score Explained

How do we know whether our Linear Regression model is actually good? Training a machine learning model is only the first step. Once a model starts making predictions, we need to measure how close those predictions are to the actual values. In Lecture 5, we focus completely on the theory of Regression Evaluation Metrics and understand how we measure the performance of a Linear Regression model. 📌 In this lecture, we cover: What are evaluation metrics and why do we need them? What is prediction error? Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE) R² Score / Coefficient of Determination MAE vs MSE vs RMSE Why MSE and RMSE penalize large errors How to interpret each metric Which metrics should be lower and which should be higher? How to choose the right evaluation metric for a regression problem 🧠 By the end of this lecture, you will understand: MAE → How much are my predictions wrong on average? MSE → How much are my errors, with larger errors receiving more penalty? RMSE → How large are my prediction errors, expressed in the original unit? R² → How much variation in the target is explained by my model relative to a mean-prediction baseline? This lecture focuses on understanding the concepts and intuition behind the metrics. 👉 In the next video, we'll take these concepts into a practical Python implementation and evaluate an actual Linear Regression model using MAE, MSE, RMSE and R² Score. 📚 Machine Learning Series Lecture 5 — Regression Evaluation Metrics Linear Regression → Evaluation Metrics → Logistic Regression → Classification Evaluation Metrics #MachineLearning #LinearRegression #Regression #MAE #MSE #RMSE #R2Score #DataScience #Python #MachineLearningForBeginners