yardstick (version 0.0.5)

kap: Kappa

Description

Kappa is a similar measure to accuracy(), but is normalized by the accuracy that would be expected by chance alone and is very useful when one or more classes have large frequency distributions.

Usage

kap(data, ...)
# S3 method for data.frame
kap(data, truth, estimate, na_rm = TRUE, ...)
kap_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 kap_vec(), a single numeric value (or NA).

Multiclass

Kappa extends naturally to multiclass scenarios. Because of this, macro and micro averaging are not implemented.

References

Cohen, J. (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement. 20 (1): 37-46.

See Also

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

Examples

# NOT RUN {
# Two class
data("two_class_example")
kap(two_class_example, truth, predicted)

# Multiclass
library(dplyr)
data(hpc_cv)

hpc_cv %>%
  filter(Resample == "Fold01") %>%
  kap(obs, pred)

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

# Weighted macro averaging
hpc_cv %>%
  group_by(Resample) %>%
  kap(obs, pred, estimator = "macro_weighted")

# Vector version
kap_vec(two_class_example$truth, two_class_example$predicted)

# Making Class2 the "relevant" level
options(yardstick.event_first = FALSE)
kap_vec(two_class_example$truth, two_class_example$predicted)
options(yardstick.event_first = TRUE)

# }

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