Tidymodels class cost

I am dealing with a prediction case where the data is suffering from a strong imbalance in the binary prediction target. Is there a way of penalizing wrong predictions of the minority class with a cost matrix in TidyModels? I know that caret had this implemented, but the information I find in TidyModels is quite confusing. All I find is the baguette::class_cost() function from the experimental baguette package, which only seems to apply to bagged trees models.

Solution

Yes, you want to set a classification_cost():

library(yardstick)
#> For binary classification, the first factor level is assumed to be the event.
#> Use the argument `event_level = "second"` to alter this as needed.
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

# Two class example
data(two_class_example)

# Assuming `Class1` is our "event", this penalizes false positives heavily
costs1 <- tribble(
  ~truth,   ~estimate, ~cost,
  "Class1", "Class2",  1,
  "Class2", "Class1",  2
)

# Assuming `Class1` is our "event", this penalizes false negatives heavily
costs2 <- tribble(
  ~truth,   ~estimate, ~cost,
  "Class1", "Class2",  2,
  "Class2", "Class1",  1
)

classification_cost(two_class_example, truth, Class1, costs = costs1)
#> # A tibble: 1 × 3
#>   .metric             .estimator .estimate
#>   <chr>               <chr>          <dbl>
#> 1 classification_cost binary         0.288
classification_cost(two_class_example, truth, Class1, costs = costs2)
#> # A tibble: 1 × 3
#>   .metric             .estimator .estimate
#>   <chr>               <chr>          <dbl>
#> 1 classification_cost binary         0.260

^{Created on 2021-10-27 by the reprex package (v2.0.1)}

In tidymodels, you can use this metric either just to compute results after the fact or in tuning. Learn more here.