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 class '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. Two exceptions here are:
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.
# load libraries for models and data
library(caret)
#> 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
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)
#> # A tibble: 14 × 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 × 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 <- confusionMatrix(
six_class_sample1,
six_class_sample2
)
# summarize model fit with tidiers
tidy(six_class_cm)
#> # A tibble: 69 × 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 specificity a 0.888 NA NA NA
#> 6 pos_pred_value a 0.308 NA NA NA
#> 7 neg_pred_value a 0.816 NA NA NA
#> 8 precision a 0.308 NA NA NA
#> 9 recall a 0.2 NA NA NA
#> 10 f1 a 0.242 NA NA NA
#> # ℹ 59 more rows
tidy(six_class_cm, by_class = FALSE)
#> # 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