Detection prevalence is defined as the number of predicted positive events (both true positive and false positive) divided by the total number of predictions.

detection_prevalence(data, ...)

# S3 method for data.frame
detection_prevalence(
  data,
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  ...
)

detection_prevalence_vec(truth, estimate, estimator = NULL, na_rm = TRUE, ...)

Arguments

data

Either a data.frame containing the truth and estimate columns, or a table/matrix where the true class results should be in the columns of the table.

...

Not currently used.

truth

The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.

estimate

The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For _vec() functions, a factor vector.

estimator

One of: "binary", "macro", "macro_weighted", or "micro" to specify the type of averaging to be done. "binary" is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose "binary" or "macro" based on estimate.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For detection_prevalence_vec(), a single numeric value (or NA).

Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest by running: options(yardstick.event_first = FALSE). For multiclass extensions involving one-vs-all comparisons (such as macro averaging), this option is ignored and the "one" level is always the relevant result.

Multiclass

Macro, micro, and macro-weighted averaging is available for this metric. The default is to select macro averaging if a truth factor with more than 2 levels is provided. Otherwise, a standard binary calculation is done. See vignette("multiclass", "yardstick") for more information.

See also

Other class metrics: accuracy(), bal_accuracy(), f_meas(), j_index(), kap(), mcc(), npv(), ppv(), precision(), recall(), sens(), spec()

Examples

# Two class data("two_class_example") detection_prevalence(two_class_example, truth, predicted)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 detection_prevalence binary 0.554
# Multiclass library(dplyr) data(hpc_cv) hpc_cv %>% filter(Resample == "Fold01") %>% detection_prevalence(obs, pred)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 detection_prevalence macro 0.25
# Groups are respected hpc_cv %>% group_by(Resample) %>% detection_prevalence(obs, pred)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 detection_prevalence macro 0.25 #> 2 Fold02 detection_prevalence macro 0.25 #> 3 Fold03 detection_prevalence macro 0.25 #> 4 Fold04 detection_prevalence macro 0.25 #> 5 Fold05 detection_prevalence macro 0.25 #> 6 Fold06 detection_prevalence macro 0.25 #> 7 Fold07 detection_prevalence macro 0.25 #> 8 Fold08 detection_prevalence macro 0.250 #> 9 Fold09 detection_prevalence macro 0.25 #> 10 Fold10 detection_prevalence macro 0.25
# Weighted macro averaging hpc_cv %>% group_by(Resample) %>% detection_prevalence(obs, pred, estimator = "macro_weighted")
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 detection_prevalence macro_weighted 0.413 #> 2 Fold02 detection_prevalence macro_weighted 0.409 #> 3 Fold03 detection_prevalence macro_weighted 0.404 #> 4 Fold04 detection_prevalence macro_weighted 0.411 #> 5 Fold05 detection_prevalence macro_weighted 0.407 #> 6 Fold06 detection_prevalence macro_weighted 0.411 #> 7 Fold07 detection_prevalence macro_weighted 0.405 #> 8 Fold08 detection_prevalence macro_weighted 0.406 #> 9 Fold09 detection_prevalence macro_weighted 0.402 #> 10 Fold10 detection_prevalence macro_weighted 0.408
# Vector version detection_prevalence_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.554
# Making Class2 the "relevant" level options(yardstick.event_first = FALSE) detection_prevalence_vec(two_class_example$truth, two_class_example$predicted)
#> [1] 0.446
options(yardstick.event_first = TRUE)