fairmetrics (version 1.0.3)

eval_neg_pred_parity: Examine Negative Predictive Parity of a Model

Description

This function evaluates negative predictive predictive parity, a key fairness criterion that compares the Negative Predictive Value (NPV) between groups defined by a sensitive attribute. In other words, it assesses whether, among individuals predicted to be negative, the probability of being truly negative is equal across subgroups.

Usage

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

Value

A list containing the following elements:

• NPV_Group1: Negative Predictive Value for the first group

• NPV_Group2: Negative Predictive Value for the second group

• NPV_Diff: Difference in Negative Predictive Value

• NPV_Ratio: Ratio in Negative Predictive Value If confidence intervals are computed (confint = TRUE):

• NPV_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Negative Predictive Value

• NPV_Ratio_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the ratio in Negative Predictive Value

Arguments

data

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

outcome

Name of the outcome variable, it must be binary

group

Name of the sensitive attribute

probs

Name of the predicted outcome variable

cutoff

Threshold for the predicted outcome, default is 0.5

confint

Whether to compute 95% confidence interval, default is TRUE

bootstraps

Number of bootstrap samples, default is 2500

alpha

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

digits

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

message

Whether to print the results, default is TRUE

See Also

eval_pos_pred_parity

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 Negative Predictive Parity
eval_neg_pred_parity(
  data = test_data,
  outcome = "day_28_flg",
  group = "gender",
  probs = "pred",
  cutoff = 0.41
)
# }