pr_auc() is a metric that computes the area under the precision
recall curve. See pr_curve() for the full curve.
pr_auc(data, ...)
# S3 method for class 'data.frame'
pr_auc(
data,
truth,
...,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL
)
pr_auc_vec(
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
event_level = yardstick_event_level(),
case_weights = NULL,
...
)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", "macro", or "macro_weighted" to
specify the type of averaging to be done. "binary" is only relevant for
the two class case. The other two are general methods for calculating
multiclass metrics. The default will automatically choose "binary" or
"macro" based on truth.
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().
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 pr_auc_vec(), a single numeric value (or NA).
Macro and macro-weighted averaging is available for this metric.
The default is to select macro averaging if a truth factor with more
than 2 levels is provided. Otherwise, a standard binary calculation is done.
See vignette("multiclass", "yardstick") for more information.
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.
pr_curve() for computing the full precision recall curve.
Other class probability metrics:
average_precision(),
brier_class(),
classification_cost(),
gain_capture(),
mn_log_loss(),
roc_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.
pr_auc(two_class_example, truth, Class1)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 pr_auc binary 0.946
# ---------------------------------------------------------------------------
# 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") %>%
pr_auc(obs, VF:L)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 pr_auc macro 0.611
# 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")) %>%
pr_auc(obs, M, VF:L)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 pr_auc macro 0.611
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
pr_auc(obs, VF:L)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 pr_auc macro 0.611
#> 2 Fold02 pr_auc macro 0.620
#> 3 Fold03 pr_auc macro 0.689
#> 4 Fold04 pr_auc macro 0.680
#> 5 Fold05 pr_auc macro 0.620
#> 6 Fold06 pr_auc macro 0.650
#> 7 Fold07 pr_auc macro 0.607
#> 8 Fold08 pr_auc macro 0.650
#> 9 Fold09 pr_auc macro 0.628
#> 10 Fold10 pr_auc macro 0.603
# Weighted macro averaging
hpc_cv %>%
group_by(Resample) %>%
pr_auc(obs, VF:L, estimator = "macro_weighted")
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 pr_auc macro_weighted 0.746
#> 2 Fold02 pr_auc macro_weighted 0.743
#> 3 Fold03 pr_auc macro_weighted 0.789
#> 4 Fold04 pr_auc macro_weighted 0.754
#> 5 Fold05 pr_auc macro_weighted 0.737
#> 6 Fold06 pr_auc macro_weighted 0.743
#> 7 Fold07 pr_auc macro_weighted 0.748
#> 8 Fold08 pr_auc macro_weighted 0.756
#> 9 Fold09 pr_auc macro_weighted 0.711
#> 10 Fold10 pr_auc macro_weighted 0.737
# Vector version
# Supply a matrix of class probabilities
fold1 <- hpc_cv %>%
filter(Resample == "Fold01")
pr_auc_vec(
truth = fold1$obs,
matrix(
c(fold1$VF, fold1$F, fold1$M, fold1$L),
ncol = 4
)
)
#> [1] 0.6109931