AI/Machine Learning Engineer at Albertsons | Machine Learning | Deep Learning | NLP
🚀 New Blog Alert: Understanding regression metrics can be confusing sometimes, but it’s essential if you want to evaluate the performance of your models accurately. In my latest post, I break down the most important metrics in simple terms: 🔸 MAE (Mean Absolute Error) – A straightforward way to understand the average prediction error. 🔸 MSE (Mean Squared Error) – Helps you catch larger errors by squaring the differences. 🔸 RMSE (Root Mean Squared Error) – Gives you error in the same units as your target variable, making it easier to interpret. 🔸 R² (R-Squared) – Want to know how well your model fits the data? This is the metric for you! Whether you’re just getting started with regression models or looking to deepen your knowledge, this guide will help you navigate these metrics with clarity. Check it out and feel free to leave your thoughts! 💬👇 #MachineLearning #DataScience #RegressionMetrics #AI #MAE #RMSE #R²