Plot Fairness Accuracy Trade-offs

Provides visualization wrt. trade-offs between fairness and accuracy metrics across learners and resampling iterations. This can assist in gauging the optimal model from a set of options along with estimates of variance (through individual resampling iterations).

Usage

fairness_accuracy_tradeoff(object, ...)

Arguments

object

(PredictionClassif | BenchmarkResult | ResampleResult)
The binary class prediction object that will be evaluated.

• If provided a PredictionClassif. Then only one point will indicate the accuracy and fairness metrics for the current predictions. Requires also passing a Task.

• If provided a ResampleResult. Then the plot will compare the accuracy and fairness metrics for the same model, but different resampling iterations as well as the aggregate indicated by a cross.

• If provided a BenchmarkResult. Then the plot will compare the accuracy and fairness metrics for all models and all resampling iterations. Points are colored according to the learner_id and faceted by task_id. The aggregated score is indicated by a cross.

...

Arguments to be passed to methods. Such as:

• fairness_measure (Measure)
  The fairness measures that will evaluated. Default measure set to be msr("fairness.fpr")

• accuracy_measure (Measure)
  The accuracy measure that will evaluated. Default measure set to be msr("classif.acc").

• task (TaskClassif)
  The data task that contains the protected column, only required when the class of object is (PredictionClassif)

Value

A 'ggplot2' object.

Protected Attributes

The protected attribute is specified as a col_role in the corresponding Task():
<Task>$col_roles$pta = "name_of_attribute"
This also allows specifying more than one protected attribute, in which case fairness will be considered on the level of intersecting groups defined by all columns selected as a predicted attribute.

Examples

library("mlr3")
library("mlr3learners")
library("ggplot2")

# Setup the Fairness measure and tasks
task = tsk("adult_train")$filter(1:500)
learner = lrn("classif.ranger", predict_type = "prob")
fairness_measure = msr("fairness.tpr")

# Example 1 - A single prediction
learner$train(task)
predictions = learner$predict(task)
fairness_accuracy_tradeoff(predictions, fairness_measure, task = task)


# Example2 - A benchmark
design = benchmark_grid(
  tasks = task,
  learners = lrns(c("classif.featureless", "classif.rpart"),
    predict_type = "prob", predict_sets = c("train", "test")),
  resamplings = rsmps("cv", folds = 2)
)
bmr = benchmark(design)
fairness_accuracy_tradeoff(bmr, fairness_measure)