100 Days of Machine Learning | CampusX - Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score

Understand key metrics for evaluating regression models in this video. We cover Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R2 Score, and Adjusted R2 Score. Learn how these metrics help assess the accuracy and performance of your regression models. Code Used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day49-regression-metrics ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 01:32 - MAE(Mean Absolute Error) 07:41 - MSE(Mean squared Error) 12:29 - RSME(Root Mean Squared Error) 15:52 - R2 SCORE 27:06 - Adjusted R2 SCORE 33:41 - Code for the above Matrix