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

Usage

detection_prevalence(data, ...)

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

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

Arguments

data

Either a data.frame containing the columns specified by the truth and estimate arguments, 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.

case_weights

The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector.

event_level

A single string. Either "first" or "second" to specify which level of truth to consider as the "event". This argument is only applicable when estimator = "binary". The default uses an internal helper that generally defaults to "first", however, if the deprecated global option yardstick.event_first is set, that will be used instead with a warning.

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 when computing binary classification metrics. In yardstick, the default is to use the first level. To alter this, change the argument event_level to "second" to consider the last level of the factor the level of interest. 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()

Author

Max Kuhn

Examples

# Two class
data("two_class_example")
detection_prevalence(two_class_example, truth, predicted)
#> # A tibble: 1 × 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 × 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 × 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           0.25
#>  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 × 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
detection_prevalence_vec(
  two_class_example$truth,
  two_class_example$predicted,
  event_level = "second"
)
#> [1] 0.446