loss_functions: loss functions

In xnet: Two-Step Kernel Ridge Regression for Network Predictions

Description

These functions can be used as loss functions in tune. Currently, two functions are provided: a function calculating the classic mean squared error (loss_mse) and a function calculating 1 - AUC (loss_auc).

Usage

loss_mse(Y, LOO, na.rm = FALSE)

loss_auc(Y, LOO)

Arguments

Y	the label matrix with observed responses
LOO	the leave-one-out crossvalidation (or predictions if you must). This one can be calculated by the function loo.
na.rm	a logical value

Details

The AUC is calculated by sorting the Y matrix based on the order of the values in the LOO matrix. The false and true positive rates are calculated solely based on that ordering, which allows for values in LOO outside the range [0,1]. It's a naive implementation which is good enough for tuning, but shouldn't be used as a correct value for 1 - auc in case the values in LOO are outside the range [0,1].

Note

The function loss_auc should only be used for a Y matrix that contains solely the values 0 and 1.

See Also

tune for application of the loss function

Examples

x <- c(1,0,0,1,0,0,1,0,1)
y <- c(0.8,-0.1,0.2,0.2,0.4,0.01,1.12,0.9,0.9)
loss_mse(x,y)
loss_auc(x,y)

xnet documentation built on Feb. 4, 2020, 9:10 a.m.