h2o (version 3.44.0.3)

h2o.fair_pd_plot: 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)

Value

ggplot2 object

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.

Examples

if (FALSE) {
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")
}