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Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

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Mean Squared Error and Root Mean Squared Error

MSE is the average of the squared error that is used as the loss function for least squares regression:

It is the sum, over all the data points, of the square of the difference between the predicted and actual target variables, divided by the number of data points.

RMSE is the square root of MSE. MSE is measured in units that are the square of the target variable, while RMSE is measured in the same units as the target variable. Due to its formulation, MSE, just like the squared loss function that it derives from, effectively penalizes larger errors more severely.

In order to evaluate our predictions ...

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