broom (version 0.7.5)

tidy.confusionMatrix: Tidy a(n) confusionMatrix object

Description

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.

Usage

# 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.level = 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.

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.

See Also

tidy(), caret::confusionMatrix()

Examples

# NOT RUN {
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)
# }