mlr3fairness (version 0.4.0)

fairness_prediction_density: Probability Density Plot

Description

Visualizes per-subgroup densities across learners, task and class. The plot is a combination of boxplot and violin plot. The y-axis shows the levels in protected columns. And the x-axis shows the predicted probability. The title for the plot will demonstrate which class for predicted probability.

Usage

fairness_prediction_density(object, ...)

Value

A 'ggplot2' object.

Arguments

object

(mlr3::PredictionClassif | mlr3::ResampleResult | mlr3::BenchmarkResult)
The binary class prediction object that will be evaluated. If mlr3::PredictionClassif, a mlr3::Task is required.

...

The arguments to be passed to methods, such as:

• task (mlr3::TaskClassif)
  The data task that contains the protected column.

• type character
  The plot type. Either violin or density.

Protected Attributes

The protected attribute is specified as a col_role in the corresponding mlr3::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")

task = tsk("adult_train")$filter(1:500)
learner = lrn("classif.rpart", predict_type = "prob", cp = 0.001)
learner$train(task)

# For prediction
predictions = learner$predict(task)
fairness_prediction_density(predictions, task)

# For resampling
rr = resample(task, learner, rsmp("cv"))
fairness_prediction_density(rr)