Receiver operator curve
roc_curve() constructs the full ROC curve and returns a
tibble. See roc_auc() for the area under the ROC curve.
roc_curve(data, ...) ## S3 method for class 'data.frame' roc_curve( data, truth, ..., options = list(), na_rm = TRUE, event_level = yardstick_event_level() ) autoplot.roc_df(object, ...)
data |
A |
... |
A set of unquoted column names or one or more
|
truth |
The column identifier for the true class results
(that is a |
options |
A |
na_rm |
A |
event_level |
A single string. Either |
object |
The |
roc_curve() computes the sensitivity at every unique
value of the probability column (in addition to infinity and
minus infinity). If a smooth ROC curve was produced, the unique
observed values of the specificity are used to create the curve
points. In either case, this may not be efficient for large data
sets.
There is a ggplot2::autoplot()
method for quickly visualizing the curve. This works for
binary and multiclass output, and also works with grouped data (i.e. from
resamples). See the examples.
A tibble with class roc_df or roc_grouped_df having
columns specificity and sensitivity.
If an ordinary (i.e. non-smoothed) curve
is used, there is also a column for .threshold.
If a multiclass truth column is provided, a one-vs-all
approach will be taken to calculate multiple curves, one per level.
In this case, there will be an additional column, .level,
identifying the "one" column in the one-vs-all calculation.
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.
Max Kuhn
Compute the area under the ROC curve with roc_auc().
Other curve metrics:
gain_curve(),
lift_curve(),
pr_curve()
# --------------------------------------------------------------------------- # 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_curve(two_class_example, truth, Class1) # --------------------------------------------------------------------------- # `autoplot()` # Visualize the curve using ggplot2 manually library(ggplot2) library(dplyr) roc_curve(two_class_example, truth, Class1) %>% ggplot(aes(x = 1 - specificity, y = sensitivity)) + geom_path() + geom_abline(lty = 3) + coord_equal() + theme_bw() # Or use autoplot autoplot(roc_curve(two_class_example, truth, Class1)) ## Not run: # Multiclass one-vs-all approach # One curve per level hpc_cv %>% filter(Resample == "Fold01") %>% roc_curve(obs, VF:L) %>% autoplot() # Same as above, but will all of the resamples hpc_cv %>% group_by(Resample) %>% roc_curve(obs, VF:L) %>% autoplot() ## End(Not run)
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