eval_cond_acc_equality {fairmetrics} | R Documentation |
Examine Conditional Use Accuracy Equality of a Model
Description
This function evaluates Conditional Use Accuracy Equality, a fairness criterion that requires predictive performance to be similar across groups when a model makes positive or negative predictions.
Usage
eval_cond_acc_equality(
data,
outcome,
group,
probs,
cutoff = 0.5,
confint = TRUE,
alpha = 0.05,
bootstraps = 2500,
digits = 2,
message = TRUE
)
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 |
alpha |
The 1 - significance level for the confidence interval, default is 0.05 |
bootstraps |
Number of bootstrap samples, default is 2500 |
digits |
Number of digits to round the results to, default is 2 |
message |
Logical; if TRUE (default), prints a textual summary of the
fairness evaluation. Only works if |
Value
A list containing the following elements:
PPV_Group1: Positive Predictive Value for the first group
PPV_Group2: Positive Predictive Value for the second group
PPV_Diff: Difference in Positive Predictive Value
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 If confidence intervals are computed (
confint = TRUE
):PPV_Diff_CI: A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the difference in Positive Predictive Value
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
See Also
Examples
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 Conditional Use Accuracy Equality
eval_cond_acc_equality(
data = test_data,
outcome = "day_28_flg",
group = "gender",
probs = "pred",
cutoff = 0.41
)