linex {mlr3measures} | R Documentation |
Linear-Exponential Loss (per observation)
Description
Measure to compare true observed response with predicted response in regression tasks.
Note that this is an unaggregated measure, returning the losses per observation.
Usage
linex(truth, response, a = -1, b = 1, ...)
Arguments
truth |
( |
response |
( |
a |
( |
b |
( |
... |
( |
Details
The Linear-Exponential Loss is defined as
b (\exp (t_i - r_i) - a (t_i - r_i) - 1),
where a \neq 0, b > 0
.
Value
Performance value as numeric(length(truth))
.
Meta Information
Type:
"regr"
Range (per observation):
[0, \infty)
Minimize (per observation):
TRUE
Required prediction:
response
References
Varian, R. H (1975). “A Bayesian Approach to Real Estate Assessment.” In Fienberg SE, Zellner A (eds.), Studies in Bayesian Econometrics and Statistics: In Honor of Leonard J. Savage, 195–208. North-Holland, Amsterdam.
See Also
Other Regression Measures:
ae()
,
ape()
,
bias()
,
ktau()
,
mae()
,
mape()
,
maxae()
,
maxse()
,
medae()
,
medse()
,
mse()
,
msle()
,
pbias()
,
pinball()
,
rae()
,
rmse()
,
rmsle()
,
rrse()
,
rse()
,
rsq()
,
sae()
,
se()
,
sle()
,
smape()
,
srho()
,
sse()
Examples
set.seed(1)
truth = 1:10
response = truth + rnorm(10)
linex(truth, response)