Demystifying Regression Metrics: MAE, MSE, RMSE & More Explained

In the world of data science and machine learning, building a regression model is only half the battle. The other half? Measuring how well your model performs. That’s where regression metrics come in. These metrics provide critical insights into the accuracy of your predictions and help you determine if your model is good enough or needs improvement.

In this post, we’ll demystify some of the most important regression metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and a few more. By the end of this article, you’ll have a clear understanding of what these metrics mean and when to use each.

Why Regression Metrics Matter

When building machine learning models for tasks like predicting house prices, stock values, or temperature, you’re often dealing with continuous data. This means your model output isn’t just right or wrong; it falls somewhere on a scale. The challenge is to quantify how far your predictions are from the true values. Regression metrics provide a numeric score to evaluate this, helping you:

• Understand model accuracy.
• Compare different models.
• Optimize your model’s performance.

Example:

Let’s say you’re predicting house prices using some linear models.

• Actual prices: [300000,400000,500000]