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.
sle(truth, response, ...)Performance value as numeric(length(truth)).
Type: "regr"
Range (per observation): \([0, \infty)\)
Minimize (per observation): TRUE
Required prediction: response
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()