Last chance! 50% off unlimited learning
Sale ends in
Matthews correlation coefficient
mcc(data, ...)# S3 method for data.frame
mcc(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
mcc_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
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).
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.
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.
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.
A logical value indicating whether NA
values should be stripped before the computation proceeds.
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().
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.
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.
Max Kuhn
Giuseppe, J. (2012). "A Comparison of MCC and CEN Error Measures in Multi-Class Prediction". PLOS ONE. Vol 7, Iss 8, e41882.
Other class metrics:
accuracy(),
bal_accuracy(),
detection_prevalence(),
f_meas(),
j_index(),
kap(),
npv(),
ppv(),
precision(),
recall(),
sens(),
spec()
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)
Run the code above in your browser using DataLab