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, ...)
x | An object of class |
---|---|
by_class | Logical indicating whether or not to show performance
measures broken down by class. Defaults to |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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.
if (requireNamespace("caret", quietly = TRUE)) { library(caret) 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) 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 <- caret::confusionMatrix( six_class_sample1, six_class_sample2 ) tidy(six_class_cm) tidy(six_class_cm, by_class = FALSE) } #> 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 #> # 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