Machine Learning Data Science Day 25: Regression Metrics - MAE | MSE | RMSE | R2

Welcome to Day 25 of our Machine Learning Data Science series! In this session, we dive into regression metrics, essential tools for evaluating the performance of regression models. Join us as we explore Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²).
📊 Understanding Regression Metrics: Learn what these metrics are and why they are crucial for assessing regression models.
🔍 MAE (Mean Absolute Error): Discover how MAE measures the average magnitude of errors in predictions, without considering their direction.
📈 MSE (Mean Squared Error): Understand how MSE gives a higher weight to larger errors, making it sensitive to outliers.
📉 RMSE (Root Mean Squared Error): Explore RMSE, which provides a measure of the average magnitude of errors, combining the benefits of MAE and MSE.
📐 R² (R-squared): Learn about R², which indicates the proportion of the variance in the dependent variable that is predictable from the independent variables.
🛠️ Hands-On Examples: Follow along with practical examples and exercises to see how these metrics are calculated and interpreted.
🔗 Resources and Code: Access additional resources and the source code used in this lecture to enhance your learning experience.
GitHub Link for Day 25 Notes: github.com/The...
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Whether you're new to regression analysis or looking to refine your understanding of these metrics, this video will provide you with the knowledge and skills to effectively evaluate your regression models. Don’t forget to like, comment, and subscribe for more lectures and tutorials in our Machine Learning Data Science series!
#MachineLearning #DataScience #RegressionMetrics #MAE #MSE #RMSE #R2 #LearnMachineLearning #DataScienceLecture #ModelEvaluation #PythonProgramming

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