kap: Kappa

In yardstick: Tidy Characterizations of Model Performance

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,
  case_weights = NULL,
  ...
)

kap_vec(
  truth,
  estimate,
  weighting = "none",
  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.
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.
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 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.

See Also

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

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