yardstick: summary.conf_mat – R documentation

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summary.conf_mat

Summary Statistics for Confusion Matrices

Description

Various statistical summaries of confusion matrices are produced and returned in a tibble. These include those shown in the help pages for sens(), recall(), and accuracy(), among others.

Usage

## S3 method for class 'conf_mat'
summary(
  object,
  prevalence = NULL,
  beta = 1,
  estimator = NULL,
  event_level = yardstick_event_level(),
  ...
)

Arguments

`object`	An object of class `conf_mat()`.
`prevalence`	A number in `(0, 1)` for the prevalence (i.e. prior) of the event. If left to the default, the data are used to derive this value.
`beta`	A numeric value used to weight precision and recall for `f_meas()`.
`estimator`	One of: `"binary"`, `"macro"`, `"macro_weighted"`, or `"micro"` to specify the type of averaging to be done. `"binary"` is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose `"binary"` or `"macro"` based on `estimate`.
`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.
`...`	Not currently used.

Value

A tibble containing various classification metrics.

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.

Examples

data("two_class_example")

cmat <- conf_mat(two_class_example, truth = "truth", estimate = "predicted")
summary(cmat)
summary(cmat, prevalence = 0.70)

library(dplyr)
library(purrr)
library(tidyr)
data("hpc_cv")

# Compute statistics per resample then summarize
all_metrics <- hpc_cv %>%
  group_by(Resample) %>%
  conf_mat(obs, pred) %>%
  mutate(summary_tbl = map(conf_mat, summary)) %>%
  unnest(summary_tbl)

all_metrics %>%
  group_by(.metric) %>%
  summarise(
    mean = mean(.estimate, na.rm = TRUE),
    sd = sd(.estimate, na.rm = TRUE)
  )

yardstick

Tidy Characterizations of Model Performance

v0.0.8

MIT + file LICENSE

Authors

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

Initial release