mae: Mean absolute error (MAE)

In stschn/deepANN: Neural Network Toolbox

Mean absolute error (MAE)

Description

Mean absolute error (MAE)

Usage

mae(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, MAE is also known as L1 loss function. In opposite to MSE, MAE is more robust to outliers.

Value

Mean absolute error.

See Also

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

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