Calculates the per-observation 0/1 (zero-one) loss as $$
\mathbf{1} (t_i \neq r_1).
$$
The 1/0 (one-zero) loss is equal to 1 - zero-one and calculated as $$
\mathbf{1} (t_i = r_i).
$$
Measure to compare true observed labels with predicted
labels
in multiclass classification tasks.
Note that this is an unaggregated measure, returning the losses per observation.
Usage
zero_one(truth, response, ...)
one_zero(truth, response, ...)
Arguments
- truth
(factor())
True (observed) labels.
Must have the same levels and length as response.
- response
(factor())
Predicted response labels.
Must have the same levels and length as truth.
- ...
(any)
Additional arguments. Currently ignored.
Value
Performance value as numeric(length(truth)).
Type: "classif"
Range (per observation): \([0, 1]\)
Minimize (per observation): TRUE
Required prediction: response