mse: Mean squared error (MSE)

In stschn/deepANN: Neural Network Toolbox

Mean squared error (MSE)

Description

Mean squared error (MSE)

Usage

mse(actuals, preds, na.rm = FALSE)

Arguments

actuals	A numeric vector of actual values.
preds	A numeric vector of prediction values.
na.rm	A logical value indicating whether actual and prediction pairs with at least one NA value should be ignored.

Details

In Machine and Deep Learning, MSE is also known as L2 loss function.

Value

Mean squared error.

See Also

Other Metrics: accuracy(), cross_entropy(), dice(), entropy(), erf(), erfc(), erfcinv(), erfinv(), gini_impurity(), huber_loss(), iou(), log_cosh_loss(), mae(), mape(), msle(), quantile_loss(), rmse(), rmsle(), rmspe(), sse(), stderror(), vc(), wape(), wmape()

stschn/deepANN documentation built on June 25, 2024, 7:27 a.m.