What is Root Mean Squared Error (RMSE) in Machine Learning?

One of the most commonly used evaluation metrics for regression problems is RMSE, which stands for root mean squared error.

How do you calculate RMSE?
As stated in its abbreviation, RMSE is literally the square root of the average of the squared error, error being the difference between the actual target and the model's prediction.

How do you interpret RMSE?
In general, the lower the RMSE, the better the model performance is. And if your model RMSE is zero, then it's a perfect fit, or it might be a trap, so you might wanna go back and recheck.

When do you use RMSE?
RMSE is highly sensitive to outliers, so if your data does not contain any outliers, and if your model is not expected to deal with these outliers, you can go ahead and use RMSE.

Other common metrics similar to RMSE are MSE or mean squared error used when the error difference is very small. And RMSLE, which stands for root mean squared logarithmic error used when the target column is exponentially distributed.

To know more stay tuned!

[JatinKumar-cnwt]

Thanks for making this type of content