Percent Bias

Measure to compare true observed response with predicted response in regression tasks.

Usage

pbias(truth, response, sample_weights = NULL, na_value = NaN, ...)

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.
sample_weights: (numeric())
Vector of non-negative and finite sample weights. Must have the same length as truth. The vector gets automatically normalized to sum to one. Defaults to equal sample weights.
na_value: (numeric(1))
Value that should be returned if the measure is not defined for the input (as described in the note). Default is NaN.
...: (any)
Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Details

The Percent Bias is defined as $\frac{1}{n} \sum_{i = 1}^{n} w_{i} \frac{(t_{i} - r_{i})}{| t_{i} |},$ where $w_{i}$ are normalized sample weights. Good predictions score close to 0.

Meta Information

Type: "regr"
Range: $(- \infty, \infty)$
Minimize: NA
Required prediction: response

Examples

set.seed(1)
truth = 1:10
response = truth + rnorm(10)
pbias(truth, response)
#> [1] 0.02898378