compute_metrics {mlr3fairness}

Compute metrics for non-mlr3 predictions.

Description

Allows computing metrics for predictions that do not stem from mlr3, and were e.g. being made by models outside of mlr3. Currently only classif and regr - style predictions are supported.

Usage

compute_metrics(data, target, protected_attribute, prediction, metrics = NULL)

Arguments

data

(data.table)
The dataset used for predicting.

target

(character)
The name of the target variable. Must be available in data.

protected_attribute

(character)
The name(s) of the protected attributes(s). Must be available in data.

prediction

(vector)
A vector containing predictions.

metrics

(Metric|list)
(List of) mlr3 metrics to apply.

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")
# Get adult data as a data.table
train = tsk("adult_train")$data()
mod = rpart::rpart(target ~ ., train)

# Predict on test data
test = tsk("adult_test")$data()
yhat = predict(mod, test, type = "vector")

# Convert to a factor with the same levels
yhat = as.factor(yhat)
levels(yhat) = levels(test$target)

compute_metrics(
  data = test, 
  target = "target",
  prediction = yhat,
  protected_attribute = "sex",
  metrics = msr("fairness.acc")
)