Calculates the per-observation squared error as $$
\left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2.
$$
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
sle(truth, response, ...)
Arguments
- truth
(numeric())
True (observed) values.
Must have the same length as response.
- response
(numeric())
Predicted response values.
Must have the same length as truth.
- ...
(any)
Additional arguments. Currently ignored.
Value
Performance value as numeric(length(truth)).
Type: "regr"
Range (per observation): \([0, \infty)\)
Minimize (per observation): TRUE
Required prediction: response
See also
Other Regression Measures:
ae(),
ape(),
bias(),
ktau(),
linex(),
mae(),
mape(),
maxae(),
maxse(),
medae(),
medse(),
mse(),
msle(),
pbias(),
pinball(),
rae(),
rmse(),
rmsle(),
rrse(),
rse(),
rsq(),
sae(),
se(),
smape(),
srho(),
sse()