Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for confusionMatrix
tidy(x, by_class = TRUE, ...)
An object of class confusionMatrix
created by a call to
caret::confusionMatrix()
.
Logical indicating whether or not to show performance
measures broken down by class. Defaults to TRUE
. When by_class = FALSE
only returns a tibble with accuracy, kappa, and McNemar statistics.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
A tibble::tibble()
with columns:
The class under consideration.
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The name of the regression term.
P-value for accuracy and kappa statistics.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("caret", quietly = TRUE)) {
# load libraries for models and data
library(caret)
set.seed(27)
# generate data
two_class_sample1 <- as.factor(sample(letters[1:2], 100, TRUE))
two_class_sample2 <- as.factor(sample(letters[1:2], 100, TRUE))
two_class_cm <- confusionMatrix(
two_class_sample1,
two_class_sample2
)
# summarize model fit with tidiers
tidy(two_class_cm)
tidy(two_class_cm, by_class = FALSE)
# multiclass example
six_class_sample1 <- as.factor(sample(letters[1:6], 100, TRUE))
six_class_sample2 <- as.factor(sample(letters[1:6], 100, TRUE))
six_class_cm <- confusionMatrix(
six_class_sample1,
six_class_sample2
)
# summarize model fit with tidiers
tidy(six_class_cm)
tidy(six_class_cm, by_class = FALSE)
}
#> Registered S3 method overwritten by 'lava':
#> method from
#> print.equivalence partitions
#> Registered S3 methods overwritten by 'pROC':
#> method from
#> print.roc btergm
#> plot.roc btergm
#> Loading required package: lattice
#>
#> Attaching package: ‘lattice’
#> The following object is masked from ‘package:boot’:
#>
#> melanoma
#>
#> Attaching package: ‘caret’
#> The following object is masked from ‘package:survival’:
#>
#> cluster
#> The following object is masked from ‘package:purrr’:
#>
#> lift
#> # A tibble: 3 × 5
#> term estimate conf.low conf.high p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 accuracy 0.2 0.127 0.292 0.795
#> 2 kappa 0.0351 NA NA NA
#> 3 mcnemar NA NA NA 0.873