mse

Computes Mean Squared Error between two vectors. It is the average of the error (squared to get rid of negative sign) made by the regression model, where the best value is 0 and the worst value tends to infinity (it increases as the deviation between predicted and actual value increases).

Attention: Available only with Activate commercial edition.

Syntax

Score = mse(targets,predictions)

Inputs

targets
Actual label for each observation.
Type: double
Dimension: vector
predictions
Predicted value for each observation.
Type: double
Dimension: vector

Outputs

Score
MSE of the regression model.
Type: double
Dimension: scalar

Example

Usage of mse

targets = [3.14, 0.1, 50, -2.5];
predictions = [3.1, 0.5, 50.3, -5];
score = mse(targets, predictions);
> score
score = 1.6254