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roc_auc

Area under the receiver operator curve

Description

roc_auc() is a metric that computes the area under the ROC curve. See roc_curve() for the full curve.

Usage

roc_auc(data, ...)

## S3 method for class 'data.frame'
roc_auc(
  data,
  truth,
  ...,
  options = list(),
  estimator = NULL,
  na_rm = TRUE,
  event_level = yardstick_event_level()
)

roc_auc_vec(
  truth,
  estimate,
  options = list(),
  estimator = NULL,
  na_rm = TRUE,
  event_level = yardstick_event_level(),
  ...
)

Arguments

data

A data.frame containing the truth and estimate columns.
...

A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. If truth is binary, only 1 column should be selected. Otherwise, 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.
options

A list of named options to pass to pROC::roc() such as smooth. These options should not include response, predictor, levels, quiet, or direction.
estimator

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

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

If truth is binary, a numeric vector of class probabilities corresponding to the "relevant" class. Otherwise, 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.

Details

The underlying direction option in pROC::roc() is forced to direction = "<". This computes the ROC curve assuming that the estimate values are the probability that the "event" occurred, which is what they are always assumed to be in yardstick.

Generally, an ROC AUC value is between 0.5 and 1, with 1 being a perfect prediction model. If your value is between 0 and 0.5, then this implies that you have meaningful information in your model, but it is being applied incorrectly because doing the opposite of what the model predicts would result in an AUC >0.5.

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_auc_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

The default multiclass method for computing roc_auc() is to use the method from Hand, Till, (2001). Unlike macro-averaging, this method is insensitive to class distributions like the binary ROC AUC case. Additionally, while other multiclass techniques will return NA if any levels in truth occur zero times in the actual data, the Hand-Till method will simply ignore those levels in the averaging calculation, with a warning.

Macro and macro-weighted averaging are still provided, even though they are not the default. In fact, macro-weighted averaging corresponds to the same definition of multiclass AUC given by Provost and Domingos (2001).

Author(s)

Max Kuhn

References

Hand, Till (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems". Machine Learning. Vol 45, Iss 2, pp 171-186.

Fawcett (2005). "An introduction to ROC analysis". Pattern Recognition Letters. 27 (2006), pp 861-874.

Provost, F., Domingos, P., 2001. "Well-trained PETs: Improving probability estimation trees", CeDER Working Paper #IS-00-04, Stern School of Business, New York University, NY, NY 10012.

See Also

roc_curve() for computing the full ROC curve.

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

Examples

# ---------------------------------------------------------------------------
# Two class example

# `truth` is a 2 level factor. The first level is `"Class1"`, which is the
# "event of interest" by default in yardstick. See the Relevant Level
# section above.
data(two_class_example)

# Binary metrics using class probabilities take a factor `truth` column,
# and a single class probability column containing the probabilities of
# the event of interest. Here, since `"Class1"` is the first level of
# `"truth"`, it is the event of interest and we pass in probabilities for it.
roc_auc(two_class_example, truth, Class1)

# ---------------------------------------------------------------------------
# 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_auc(obs, VF:L)

# 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_auc(obs, M, VF:L)

# Groups are respected
hpc_cv %>%
  group_by(Resample) %>%
  roc_auc(obs, VF:L)

# Weighted macro averaging
hpc_cv %>%
  group_by(Resample) %>%
  roc_auc(obs, VF:L, estimator = "macro_weighted")

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

roc_auc_vec(
   truth = fold1$obs,
   matrix(
     c(fold1$VF, fold1$F, fold1$M, fold1$L),
     ncol = 4
   )
)

# ---------------------------------------------------------------------------
# Options for `pROC::roc()`

# Pass options via a named list and not through `...`!
roc_auc(
  two_class_example,
  truth = truth,
  Class1,
  options = list(smooth = TRUE)
)
yardstick

Tidy Characterizations of Model Performance

v0.0.8
MIT + file LICENSE
Authors
Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]
Initial release

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