mse: Mean Square Error (MSE)

In nikitagusarov/performancer: Performance Metrics for Discrete Choice Analysis

Mean Square Error (MSE)

Description

Compute Mean Square Error (MSE). The squared loss, in a sense, quantifies the error (or alternatively closeness) of the predicted label to the true label. When specialized to the case of probabilistic classifiers, this then can be interpreted as a reliability measure.

Usage

mse(y_real, y_predicted, rowsums = TRUE)

Arguments

y_real	Observed values (integers) to compare with (in matrix format for multiclass classification).
y_predicted	Predicte values (probabiblities by class).
rowsums	Whether in case of multiple classes in dataframe the metrics should be summed across classes before calculating the mean.

Value

integer value of Mean Square Error (MSE)

nikitagusarov/performancer documentation built on Jan. 12, 2023, 12:19 a.m.