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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 class 'data.frame'
kap(data, truth, estimate, weighting = "none", na_rm = TRUE, ...)

kap_vec(truth, estimate, weighting = "none", 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.
`weighting`	A weighting to apply when computing the scores. One of: `"none"`, `"linear"`, or `"quadratic"`. Linear and quadratic weighting penalizes mis-predictions that are "far away" from the true value. Note that distance is judged based on the ordering of the levels in `truth` and `estimate`. It is recommended to provide ordered factors for `truth` and `estimate` to explicitly code the ordering, but this is not required. In the binary case, all 3 weightings produce the same value, since it is only ever possible to be 1 unit away from the true value.
`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.

Author(s)

Max Kuhn

Jon Harmon

References

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

Cohen, J. (1968). "Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit". Psychological Bulletin. 70 (4): 213-220.

Examples

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

# Two class
kap(two_class_example, truth, predicted)

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

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

yardstick

Tidy Characterizations of Model Performance

v0.0.8

MIT + file LICENSE

Authors

Max Kuhn [aut], Davis Vaughan [aut, cre], RStudio [cph]

Initial release

kap