eval_neg_class_bal {fairmetrics} | R Documentation |
Examine Balance for Negative Class of a Model
Description
This function evaluates Balance for the Negative Class, a fairness criterion
that checks whether the model assigns similar predicted probabilities across groups
among individuals whose true outcome is negative (i.e. Y = 0
).
Usage
eval_neg_class_bal(
data,
outcome,
group,
probs,
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 |
group |
Name of the sensitive attribute |
probs |
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 |
Logical; if TRUE (default), prints a textual summary of the
fairness evaluation. Only works if |
Value
A list containing the following elements:
Average predicted probability for Group 1
Average predicted probability for Group 2
Difference in average predicted probability
Ratio in average predicted probability 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 average predicted probability
A vector of length 2 containing the lower and upper bounds of the 95% confidence interval for the ratio in average predicted probability
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.
# Evaluate Balance for Negative Class
eval_neg_class_bal(
data = test_data,
outcome = "day_28_flg",
group = "gender",
probs = "pred"
)