h2o.fair_pd_plot: Partial dependence plot per protected group.

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

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.