Evaluation Metrics for Regression Models

Once a regression model makes predictions, you need a way to measure how accurate they are. This is where evaluation metrics come in.

Imagine a weather app predicting tomorrow's temperature as 30 degrees, but the actual temperature is 32 degrees. That two-degree gap is exactly what evaluation metrics are designed to measure.

Common regression evaluation metrics

• Mean Absolute Error: It measures the average size of prediction errors, without considering direction.
• Mean Squared Error: This metric squares the errors first, so bigger mistakes get penalized more.
• Root Mean Squared Error: It converts the error back to the original unit, making it easier to read.
• R-squared Score: This score highlights how well the model explains the actual variation in the data.