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Regression measure defined as $$ \frac{1}{n} \sum_{i=1}^n \frac{\left( t_i - r_i \right)}{\left| t_i \right|}. $$ Good predictions score close to 0.
pbias(truth, response, na_value = NaN, ...)
:: numeric() True (observed) values. Must have the same length as response.
numeric()
response
:: numeric() Predicted response values. Must have the same length as truth.
truth
:: numeric(1) Value that should be returned if the measure is not defined for the input (as described in the note). Default is NaN.
numeric(1)
NaN
:: any Additional arguments. Currently ignored.
any
Performance value as numeric(1).
Type: "regr"
"regr"
Range: \((-\infty, \infty)\)
Minimize: NA
NA
Required prediction: response
Other Regression Measures: bias(), ktau(), mae(), mape(), maxae(), maxse(), medae(), medse(), mse(), msle(), rae(), rmse(), rmsle(), rrse(), rse(), rsq(), sae(), smape(), srho(), sse()
bias()
ktau()
mae()
mape()
maxae()
maxse()
medae()
medse()
mse()
msle()
rae()
rmse()
rmsle()
rrse()
rse()
rsq()
sae()
smape()
srho()
sse()
# NOT RUN { set.seed(1) truth = 1:10 response = truth + rnorm(10) pbias(truth, response) # }
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