h2o.fair_pd_plot: Partial dependence plot per protected group.

h2o.fair_pd_plotR Documentation

Partial dependence plot per protected group.

Description

Partial dependence plot per protected group.

Usage

h2o.fair_pd_plot(model, newdata, protected_columns, column, autoscale = TRUE)

Arguments

model

H2O Model Object

newdata

H2OFrame

protected_columns

List of categorical columns that contain sensitive information such as race, gender, age etc.

column

String containing column name.

autoscale

If “True“, try to guess when to use log transformation on X axis.

Value

ggplot2 object

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.fair_pd_plot(gbm, test, protected_columns, "AGE")

## End(Not run)


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