Score a segmentation against labels

Counts label errors: a positive region with no changepoint is a false negative, a negative region with one is a false positive, and a "one_change" region with two or more is a false positive as well. This is the accuracy measure supervised changepoint detection is built on, and — unlike an information criterion — it is defined by what the expert asserted rather than by a model assumption.

Usage

cpt_label_error(object, labels)

# S3 method for class 'cpt_label_error'
tidy(x, ...)

# S3 method for class 'cpt_label_error'
print(x, ...)

Arguments

object

A ggcpt object, or an integer vector of changepoint positions.

labels

A cpt_labels tibble (or anything with start/end/change columns).

x

A cpt_label_error object (for print()).

...

Ignored.

Value

A tibble with one row per label — label_id, series (the label set's series identifier, NA for a single unnamed series), start, end, change, n_changes (how many detections fell inside), status ("correct", "false_positive" or "false_negative") — carrying the totals in an errors attribute and printing them.

See also

cpt_labels(), cpt_label_error_curve(), geom_cpt_label().

Examples

set.seed(2026)
fit <- cpt_detect(c(rnorm(50), rnorm(50, 4)), method = "pelt")
labs <- cpt_labels(c(40, 70), c(60, 95), c("one_change", "no_change"))
cpt_label_error(fit, labs)
#> cpt_label_error (2 label(s))
#>   correct: 2   false positives: 0   false negatives: 0
#>   total label errors: 0
#> 
#> # A tibble: 2 × 7
#>   label_id series start   end change     n_changes status 
#>      <int> <chr>  <int> <int> <chr>          <int> <chr>  
#> 1        1 NA        40    60 one_change         1 correct
#> 2        2 NA        70    95 no_change          0 correct