mcc: Matthews correlation coefficient

In yardstick: Tidy Characterizations of Model Performance

Matthews correlation coefficient

Description

Matthews correlation coefficient

Usage

mcc(data, ...)

## S3 method for class 'data.frame'
mcc(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

mcc_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

Arguments

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.
...	Not currently used.
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.
estimate	The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For ⁠_vec()⁠ functions, a factor vector.
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. 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().

Value

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 mcc_vec(), a single numeric value (or NA).

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.

Multiclass

mcc() has a known multiclass generalization and that is computed automatically if a factor with more than 2 levels is provided. Because of this, no averaging methods are provided.

Author(s)

Max Kuhn

References

Giuseppe, J. (2012). "A Comparison of MCC and CEN Error Measures in Multi-Class Prediction". PLOS ONE. Vol 7, Iss 8, e41882.

See Also

Other class metrics: accuracy(), bal_accuracy(), detection_prevalence(), f_meas(), j_index(), kap(), npv(), ppv(), precision(), recall(), sens(), spec()

Examples

library(dplyr)
data("two_class_example")
data("hpc_cv")

# Two class
mcc(two_class_example, truth, predicted)

# Multiclass
# mcc() has a natural multiclass extension
hpc_cv %>%
  filter(Resample == "Fold01") %>%
  mcc(obs, pred)

# Groups are respected
hpc_cv %>%
  group_by(Resample) %>%
  mcc(obs, pred)

yardstick documentation built on June 22, 2024, 7:07 p.m.