Area under the ROC curve of each class against the rest, using the a priori class distribution

roc_aunp() is a multiclass metric that computes the area under the ROC curve of each class against the rest, using the a priori class distribution. This is equivalent to roc_auc(estimator = "macro_weighted").

Usage

roc_aunp(data, ...)

# S3 method for class 'data.frame'
roc_aunp(data, truth, ..., na_rm = TRUE, case_weights = NULL, options = list())

roc_aunp_vec(
  truth,
  estimate,
  na_rm = TRUE,
  case_weights = NULL,
  options = list(),
  ...
)

Arguments

data

A data.frame containing the columns specified by truth and ....

...

A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. There should be as many columns as factor levels of truth.

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.

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, hardhat::importance_weights(), or hardhat::frequency_weights().

options

[deprecated]

No longer supported as of yardstick 1.0.0. If you pass something here it will be ignored with a warning.

Previously, these were options passed on to pROC::roc(). If you need support for this, use the pROC package directly.

estimate

A matrix with as many columns as factor levels of truth. It is assumed that these are in the same order as the levels of truth.

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 roc_aunp_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

This multiclass method for computing the area under the ROC curve uses the a priori class distribution and is equivalent to roc_auc(estimator = "macro_weighted").

References

Ferri, C., Hernández-Orallo, J., & Modroiu, R. (2009). "An experimental comparison of performance measures for classification". Pattern Recognition Letters. 30 (1), pp 27-38.

See also

roc_aunu() for computing the area under the ROC curve of each class against the rest, using the uniform class distribution.

Other class probability metrics: average_precision(), brier_class(), classification_cost(), gain_capture(), mn_log_loss(), pr_auc(), roc_auc(), roc_aunu()

Author

Julia Silge

Examples

# Multiclass example

# `obs` is a 4 level factor. The first level is `"VF"`, which is the
# "event of interest" by default in yardstick. See the Relevant Level
# section above.
data(hpc_cv)

# You can use the col1:colN tidyselect syntax
library(dplyr)
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  roc_aunp(obs, VF:L)
#> # A tibble: 1 × 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 roc_aunp macro          0.880

# Change the first level of `obs` from `"VF"` to `"M"` to alter the
# event of interest. The class probability columns should be supplied
# in the same order as the levels.
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  mutate(obs = relevel(obs, "M")) %>%
  roc_aunp(obs, M, VF:L)
#> # A tibble: 1 × 3
#>   .metric  .estimator .estimate
#>   <chr>    <chr>          <dbl>
#> 1 roc_aunp macro          0.880

# Groups are respected
hpc_cv %>%
  group_by(Resample) %>%
  roc_aunp(obs, VF:L)
#> # A tibble: 10 × 4
#>    Resample .metric  .estimator .estimate
#>    <chr>    <chr>    <chr>          <dbl>
#>  1 Fold01   roc_aunp macro          0.880
#>  2 Fold02   roc_aunp macro          0.873
#>  3 Fold03   roc_aunp macro          0.906
#>  4 Fold04   roc_aunp macro          0.867
#>  5 Fold05   roc_aunp macro          0.866
#>  6 Fold06   roc_aunp macro          0.865
#>  7 Fold07   roc_aunp macro          0.868
#>  8 Fold08   roc_aunp macro          0.865
#>  9 Fold09   roc_aunp macro          0.841
#> 10 Fold10   roc_aunp macro          0.869

# Vector version
# Supply a matrix of class probabilities
fold1 <- hpc_cv %>%
  filter(Resample == "Fold01")

roc_aunp_vec(
  truth = fold1$obs,
  matrix(
    c(fold1$VF, fold1$F, fold1$M, fold1$L),
    ncol = 4
  )
)
#> [1] 0.8795121