mupet (version 0.1.0)

macro_average_specificity: Macro Average Specificity

Description

This function computes the macro-average specificity for a multi-class prediction model. It assumes that the negative class is the first one.

Usage

macro_average_specificity_vec(
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)
macro_average_specificity(data, ...)
# S3 method for data.frame
macro_average_specificity(
  data,
  truth,
  estimate,
  estimator = NULL,
  na_rm = TRUE,
  case_weights = NULL,
  event_level = "first",
  ...
)

Value

A scalar storing the value of the macro-average specificity score.

Arguments

truth

The column identifier for the true class results (that is a factor).

estimate

The column identifier for the predicted class results (that is also factor).

estimator

One of: "binary", "macro", "macro_weighted", or "micro" to specify the type of averaging to be done.

na_rm

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

case_weights

The optional column identifier for case weights.

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".

...

Currently unused.

data

Either a data.frame containing the columns specified by the truth and estimate arguments, or a table/matrix where the true class results should be in the columns of the table.

Examples

fold1 <- subset(yardstick::hpc_cv, Resample == "Fold01")
macro_average_specificity_vec(fold1$obs, fold1$pred)
macro_average_specificity(fold1, obs, pred)