pr_curve() constructs the full precision recall curve and returns a tibble. See pr_auc() for the area under the precision recall curve.

pr_curve(data, ...)

# S3 method for data.frame
pr_curve(data, truth, ..., na_rm = TRUE)

autoplot.pr_df(object, ...)



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.


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.


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


The pr_df data frame returned from pr_curve().


A tibble with class pr_df or pr_grouped_df having columns .threshold, recall, and precision.


pr_curve() computes the precision at every unique value of the probability column (in addition to infinity).

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.


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.

Relevant Level

There is no common convention on which factor level should automatically be considered the "event" or "positive" result. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest by running: options(yardstick.event_first = FALSE). 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.

See also

Compute the area under the precision recall curve with pr_auc().

Other curve metrics: gain_curve(), lift_curve(), roc_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. pr_curve(two_class_example, truth, Class1)
#> # A tibble: 501 x 3 #> .threshold recall precision #> <dbl> <dbl> <dbl> #> 1 Inf 0 1 #> 2 1.00 0.00388 1 #> 3 1.00 0.00775 1 #> 4 1.00 0.0116 1 #> 5 1.00 0.0155 1 #> 6 1.00 0.0194 1 #> 7 1.00 0.0233 1 #> 8 1.00 0.0271 1 #> 9 1.00 0.0310 1 #> 10 1.00 0.0349 1 #> # … with 491 more rows
# --------------------------------------------------------------------------- # `autoplot()` # Visualize the curve using ggplot2 manually library(ggplot2) library(dplyr) pr_curve(two_class_example, truth, Class1) %>% ggplot(aes(x = recall, y = precision)) + geom_path() + coord_equal() + theme_bw()
# Or use autoplot autoplot(pr_curve(two_class_example, truth, Class1))
# Multiclass one-vs-all approach # One curve per level hpc_cv %>% filter(Resample == "Fold01") %>% pr_curve(obs, VF:L) %>% autoplot()
# Same as above, but will all of the resamples hpc_cv %>% group_by(Resample) %>% pr_curve(obs, VF:L) %>% autoplot()