msle: Mean squared logarithmic error (MSLE)

In stschn/deepANN: Neural Network Toolbox

Mean squared logarithmic error (MSLE)

Description

Mean squared logarithmic error (MSLE)

Usage

msle(actuals, preds, alpha = 1, na.rm = FALSE)

Arguments

actuals	A numeric vector of actual values.
preds	A numeric vector of prediction values.
alpha	A numeric value (default 1) to prevent taking a negative or zero log.
na.rm	A logical value indicating whether actual and prediction pairs with at least one NA value should be ignored.

Value

Mean squared logarithmic error.

See Also

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

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