fairmetrics (version 1.0.3)

eval_acc_parity: Examine Accuracy Parity of a Model

Description

This function assesses Accuracy Parity, a fairness criterion that evaluates whether the overall accuracy of a predictive model is consistent across different groups.

Usage

eval_acc_parity(
  data,
  outcome,
  group,
  probs,
  cutoff = 0.5,
  confint = TRUE,
  alpha = 0.05,
  bootstraps = 2500,
  digits = 2,
  message = TRUE
)

Value

A list containing the following elements:

• Accuracy for Group 1

• Accuracy for Group 2

• Difference in accuracy

• Ratio in accuracy If confidence intervals are computed (confint = TRUE):

• A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in accuracy

• A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the ratio in accurac

Arguments

data

Data frame containing the outcome, predicted outcome, and sensitive attribute

outcome

Name of the outcome variable

group

Name of the sensitive attribute

probs

Predicted probabilities

cutoff

Cutoff value for the predicted probabilities

confint

Logical indicating whether to calculate confidence intervals

alpha

The 1 - significance level for the confidence interval, default is 0.05

bootstraps

Number of bootstraps to use for confidence intervals

digits

Number of digits to round the results to, default is 2

message

Whether to print the results, default is TRUE

See Also

eval_cond_acc_equality

Examples

# \donttest{
library(fairmetrics)
library(dplyr)
library(magrittr)
library(randomForest)
data("mimic_preprocessed")
set.seed(123)
train_data <- mimic_preprocessed %>%
  dplyr::filter(dplyr::row_number() <= 700)
# Fit a random forest model
rf_model <- randomForest::randomForest(factor(day_28_flg) ~ ., data = train_data, ntree = 1000)
# Test the model on the remaining data
test_data <- mimic_preprocessed %>%
  dplyr::mutate(gender = ifelse(gender_num == 1, "Male", "Female")) %>%
  dplyr::filter(dplyr::row_number() > 700)

test_data$pred <- predict(rf_model, newdata = test_data, type = "prob")[, 2]

# Fairness evaluation
# We will use sex as the sensitive attribute and day_28_flg as the outcome.
# We choose threshold = 0.41 so that the overall FPR is around 5%.

# Evaluate Accuracy Parity
eval_acc_parity(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41
)
# }