Root Mean Squared Error (RMSE)

What is Root Mean Squared Error (RMSE)?

Root Mean Squared Error (RMSE) is a commonly used metric in the artificial intelligence industry to evaluate the accuracy of predictive models. It calculates the square root of the average of the squared differences between predicted and actual values. This metric is particularly useful because it gives a relatively high weight to large errors, making it sensitive to outliers and providing a clear indication of the model's predictive performance. By returning the error in the same units as the target variable, RMSE is easy to interpret and can be directly compared with the actual data range. It is widely used in various applications such as regression analysis, machine learning, and deep learning to ensure that models are making accurate predictions.

A measure of the differences between values predicted by a model and the actual values.

Examples

• In a housing price prediction model, RMSE can help determine how close the predicted prices are to the actual selling prices. For instance, if the RMSE is $20,000, this indicates that on average, the predicted prices deviate from the actual prices by $20,000.


• In weather forecasting, RMSE is used to measure the accuracy of temperature predictions. If a model predicts the daily high temperatures and achieves an RMSE of 2 degrees Celsius, it signifies that the predictions are, on average, within 2 degrees of the actual temperatures.

Additional Information

• RMSE is sensitive to outliers, meaning that large errors have a disproportionately large effect on the metric.


• It is essential to compare RMSE with the range of the data to understand its significance. An RMSE of 5 might be excellent in some contexts but poor in others.