roc_auc() is a metric that computes the area under the ROC curve. See
roc_curve() for the full curve.
roc_auc(data, ...)
# S3 method for class 'data.frame'
roc_auc(
data,
truth,
...,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL,
options = list()
)
roc_auc_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL,
options = list(),
...
)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. If truth is binary, only 1 column should be selected,
and it should correspond to the value of event_level. Otherwise, there
should be as many columns as factor levels of truth and the ordering of
the columns should be the same as the factor levels of 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.
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" if truth is binary, "hand_till" if truth has >2 levels and
case_weights isn't specified, or "macro" if truth has >2 levels and
case_weights is specified (in which case "hand_till" isn't
well-defined).
A logical value indicating whether NA
values should be stripped before the computation proceeds.
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 defaults to "first".
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().
[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.
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.
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).
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.
Note that you can't combine estimator = "hand_till" with case_weights.
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.
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).
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.
roc_curve() for computing the full ROC curve.
Other class probability metrics:
average_precision(),
brier_class(),
classification_cost(),
gain_capture(),
mn_log_loss(),
pr_auc(),
roc_aunp(),
roc_aunu()
# ---------------------------------------------------------------------------
# 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)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.939
# ---------------------------------------------------------------------------
# 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)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc hand_till 0.813
# 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)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc hand_till 0.813
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
roc_auc(obs, VF:L)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 roc_auc hand_till 0.813
#> 2 Fold02 roc_auc hand_till 0.817
#> 3 Fold03 roc_auc hand_till 0.869
#> 4 Fold04 roc_auc hand_till 0.849
#> 5 Fold05 roc_auc hand_till 0.811
#> 6 Fold06 roc_auc hand_till 0.836
#> 7 Fold07 roc_auc hand_till 0.825
#> 8 Fold08 roc_auc hand_till 0.846
#> 9 Fold09 roc_auc hand_till 0.828
#> 10 Fold10 roc_auc hand_till 0.812
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
roc_auc(obs, VF:L, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 roc_auc macro_weighted 0.880
#> 2 Fold02 roc_auc macro_weighted 0.873
#> 3 Fold03 roc_auc macro_weighted 0.906
#> 4 Fold04 roc_auc macro_weighted 0.867
#> 5 Fold05 roc_auc macro_weighted 0.866
#> 6 Fold06 roc_auc macro_weighted 0.865
#> 7 Fold07 roc_auc macro_weighted 0.868
#> 8 Fold08 roc_auc macro_weighted 0.865
#> 9 Fold09 roc_auc macro_weighted 0.841
#> 10 Fold10 roc_auc macro_weighted 0.869
# 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
)
)
#> [1] 0.8131924