h2o.calculate_fairness_metrics: Calculate intersectional fairness metrics.

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Calculate intersectional fairness metrics.

Description

Calculate intersectional fairness metrics.

Usage

h2o.calculate_fairness_metrics(
  model,
  frame,
  protected_columns,
  reference,
  favorable_class
)

Arguments

model	H2O Model
frame	Frame used to calculate the metrics.
protected_columns	List of categorical columns that contain sensitive information such as race, gender, age etc.
reference	List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference.
favorable_class	Positive/favorable outcome class of the response.

Value

Dictionary of frames. One frame is the overview, other frames contain dependence of performance on threshold for each protected group.

Examples

## Not run: 
library(h2o)
h2o.init()
data <- h2o.importFile(paste0("https://s3.amazonaws.com/h2o-public-test-data/smalldata/",
                              "admissibleml_test/taiwan_credit_card_uci.csv"))
x <- c('LIMIT_BAL', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1',
       'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2',
       'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6')
y <- "default payment next month"
protected_columns <- c('SEX', 'EDUCATION')

for (col in c(y, protected_columns))
  data[[col]] <- as.factor(data[[col]])

splits <- h2o.splitFrame(data, 0.8)
train <- splits[[1]]
test <- splits[[2]]
reference <- c(SEX = "1", EDUCATION = "2")  # university educated man
favorable_class <- "0" # no default next month

gbm <- h2o.gbm(x, y, training_frame = train)

h2o.calculate_fairness_metrics(gbm, test, protected_columns = protected_columns,
                               reference = reference, favorable_class = favorable_class)

## End(Not run)

h2o documentation built on May 29, 2024, 4:26 a.m.