h2o.disparate_analysis: Create a frame containing aggregations of intersectional...

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

Create a frame containing aggregations of intersectional fairness across the models.

Description

Create a frame containing aggregations of intersectional fairness across the models.

Usage

h2o.disparate_analysis(
  models,
  newdata,
  protected_columns,
  reference,
  favorable_class,
  air_metric = "selectedRatio",
  alpha = 0.05
)

Arguments

models	List of H2O Models
newdata	H2OFrame
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.
air_metric	Metric used for Adverse Impact Ratio calculation. Defaults to “selectedRatio“.
alpha	The alpha level is the probability of rejecting the null hypothesis that the protected group and the reference came from the same population when the null hypothesis is true.

Value

frame containing aggregations of intersectional fairness across the models

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

aml <- h2o.automl(x, y, training_frame = train, max_models = 3)

h2o.disparate_analysis(aml, test, protected_columns = protected_columns,
                       reference = reference, favorable_class = favorable_class)

## End(Not run)

h2o documentation built on Aug. 9, 2023, 9:06 a.m.