fairness_prediction_density: Probability Density Plot

In mlr3fairness: Fairness Auditing and Debiasing for 'mlr3'

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, ...)

Arguments

object	(PredictionClassif | ResampleResult | BenchmarkResult) The binary class prediction object that will be evaluated. If PredictionClassif, a Task is required.
...	The arguments to be passed to methods, such as: task (TaskClassif) The data task that contains the protected column. type character The plot type. Either violin or density.

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")

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)

mlr3fairness documentation built on May 31, 2023, 7:22 p.m.