Matthews correlation coefficient

mcc(data, ...)

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

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

Arguments

data

Either a data.frame containing the truth and estimate columns, 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.

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. In yardstick, the default is to use the first level. To change this, a global option called yardstick.event_first is set to TRUE when the package is loaded. This can be changed to FALSE if the last level of the factor is considered the level of interest by running: options(yardstick.event_first = FALSE). 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.

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

# Two class data("two_class_example") mcc(two_class_example, truth, predicted)
#> # A tibble: 1 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 mcc binary 0.677
# Multiclass # mcc() has a natural multiclass extension library(dplyr) data(hpc_cv) hpc_cv %>% group_by(Resample) %>% mcc(obs, pred)
#> # A tibble: 10 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 mcc multiclass 0.542 #> 2 Fold02 mcc multiclass 0.521 #> 3 Fold03 mcc multiclass 0.602 #> 4 Fold04 mcc multiclass 0.519 #> 5 Fold05 mcc multiclass 0.520 #> 6 Fold06 mcc multiclass 0.494 #> 7 Fold07 mcc multiclass 0.461 #> 8 Fold08 mcc multiclass 0.538 #> 9 Fold09 mcc multiclass 0.459 #> 10 Fold10 mcc multiclass 0.498