yardstick: pr_curve – R documentation

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pr_curve

Precision recall curve

Description

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

Usage

pr_curve(data, ...)

## S3 method for class 'data.frame'
pr_curve(data, truth, ..., na_rm = TRUE, event_level = yardstick_event_level())

autoplot.pr_df(object, ...)

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.
`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.
`object`	The `pr_df` data frame returned from `pr_curve()`.

Details

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.

Value

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

Multiclass

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 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.

Author(s)

Max Kuhn

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.
pr_curve(two_class_example, truth, Class1)

# ---------------------------------------------------------------------------
# `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()

yardstick

Tidy Characterizations of Model Performance

v0.0.8

MIT + file LICENSE

Authors

Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]

Initial release