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 cross 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, ...)

Arguments

x

An object of class confusionMatrix created by a call to caret::confusionMatrix().

by_class

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.

See also

Value

A tibble::tibble() with columns:

class

The class under consideration.

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

term

The name of the regression term.

p.value

P-value for accuracy and kappa statistics.

Examples

library(caret)
#> Loading required package: lattice
#> #> Attaching package: ‘lattice’
#> The following object is masked from ‘package:boot’: #> #> melanoma
#> Registered S3 method overwritten by 'lava': #> method from #> print.equivalence partitions
#> #> Attaching package: ‘caret’
#> The following object is masked from ‘package:survival’: #> #> cluster
set.seed(27) 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 <- caret::confusionMatrix( two_class_sample1, two_class_sample2 ) tidy(two_class_cm)
#> # A tibble: 14 x 6 #> term class estimate conf.low conf.high p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 accuracy <NA> 0.52 0.418 0.621 0.619 #> 2 kappa <NA> 0.0295 NA NA NA #> 3 mcnemar <NA> NA NA NA 0.470 #> 4 sensitivity a 0.604 NA NA NA #> 5 specificity a 0.426 NA NA NA #> 6 pos_pred_value a 0.542 NA NA NA #> 7 neg_pred_value a 0.488 NA NA NA #> 8 precision a 0.542 NA NA NA #> 9 recall a 0.604 NA NA NA #> 10 f1 a 0.571 NA NA NA #> 11 prevalence a 0.53 NA NA NA #> 12 detection_rate a 0.32 NA NA NA #> 13 detection_prevalence a 0.59 NA NA NA #> 14 balanced_accuracy a 0.515 NA NA NA
tidy(two_class_cm, by_class = FALSE)
#> # A tibble: 3 x 5 #> term estimate conf.low conf.high p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 accuracy 0.52 0.418 0.621 0.619 #> 2 kappa 0.0295 NA NA NA #> 3 mcnemar NA NA NA 0.470
# 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 <- caret::confusionMatrix( six_class_sample1, six_class_sample2 ) tidy(six_class_cm)
#> # A tibble: 69 x 6 #> term class estimate conf.low conf.high p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 accuracy <NA> 0.2 0.127 0.292 0.795 #> 2 kappa <NA> 0.0351 NA NA NA #> 3 mcnemar <NA> NA NA NA 0.873 #> 4 sensitivity a 0.2 NA NA NA #> 5 sensitivity b 0.286 NA NA NA #> 6 sensitivity c 0.133 NA NA NA #> 7 sensitivity d 0.133 NA NA NA #> 8 sensitivity e 0.231 NA NA NA #> 9 sensitivity f 0.217 NA NA NA #> 10 specificity a 0.888 NA NA NA #> # … with 59 more rows
tidy(six_class_cm, by_class = FALSE)
#> # A tibble: 3 x 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