# Root Mean Square Error (RMSE)

The **root mean square error (RMSE)** is a performance measure often used to evaluate machine learning regression models by determining the similarity between two sets. The formula for RMSE is:

$RMSE(X, h) = \sqrt{\frac{1}{m}\sum_{i=1}^{m}(h(x^{(i)}) - y^{(i)})^{2}}$

Where $X$ is a matrix containing all of the feature values (excluding labels) in the test dataset with one row per instance, $h$ is the prediction function, $m$ is the number of records in the validation dataset, $X^{(i)}$ is a vector of all feature values of the i^{th} instance (within $X$), and $y^{(i)}$ is a vector of all the desired outputs (the labels).

## Broader Topics Related to Root Mean Square Error (RMSE)

### Regression Learning

A type of machine learning that classifies entities based on their characteristics