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

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

Broader Topics Related to Root Mean Square Error (RMSE)

Regression Learning

Regression Learning

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

Root Mean Square Error (RMSE) Knowledge Graph