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,
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 thetruth
andestimate
arguments, or atable
/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, afactor
vector.- estimate
The column identifier for the predicted class results (that is also
factor
). As withtruth
this can be specified different ways but the primary method is to use an unquoted variable name. For_vec()
functions, afactor
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 intruth
andestimate
. It is recommended to provide ordered factors fortruth
andestimate
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 whetherNA
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()
, orhardhat::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.
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)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 kap binary 0.675
# Multiclass
# kap() has a natural multiclass extension
hpc_cv %>%
filter(Resample == "Fold01") %>%
kap(obs, pred)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 kap multiclass 0.533
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
kap(obs, pred)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 kap multiclass 0.533
#> 2 Fold02 kap multiclass 0.512
#> 3 Fold03 kap multiclass 0.594
#> 4 Fold04 kap multiclass 0.511
#> 5 Fold05 kap multiclass 0.514
#> 6 Fold06 kap multiclass 0.486
#> 7 Fold07 kap multiclass 0.454
#> 8 Fold08 kap multiclass 0.531
#> 9 Fold09 kap multiclass 0.454
#> 10 Fold10 kap multiclass 0.492