Compute average confusion matrix across resamples

Source: R/conf_mat_resampled.R

For classification problems, conf_mat_resampled() computes a separate confusion matrix for each resample then averages the cell counts.

conf_mat_resampled(x, ..., parameters = NULL, tidy = TRUE)

Arguments

x

An object with class tune_results that was used with a classification model that was run with control_*(save_pred = TRUE).

...

Currently unused, must be empty.

parameters

A tibble with a single tuning parameter combination. Only one tuning parameter combination (if any were used) is allowed here.

tidy

Should the results come back in a tibble (TRUE) or a conf_mat object like yardstick::conf_mat() (FALSE)?

Value

A tibble or conf_mat with the average cell count across resamples.

Examples

# example code

library(parsnip)
library(rsample)
library(dplyr)

data(two_class_dat, package = "modeldata")

set.seed(2393)
res <-
  logistic_reg() |>
  set_engine("glm") |>
  fit_resamples(
    Class ~ .,
    resamples = vfold_cv(two_class_dat, v = 3),
    control = control_resamples(save_pred = TRUE)
  )

conf_mat_resampled(res)
#> # A tibble: 4 × 3
#>   Prediction Truth   Freq
#>   <fct>      <fct>  <dbl>
#> 1 Class1     Class1 123  
#> 2 Class1     Class2  25.7
#> 3 Class2     Class1  22.7
#> 4 Class2     Class2  92.3
conf_mat_resampled(res, tidy = FALSE)
#>           Class1    Class2
#> Class1 123.00000  25.66667
#> Class2  22.66667  92.33333