h2o.fair_shap_plot: SHAP summary plot for one feature with protected groups on...

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

SHAP summary plot for one feature with protected groups on y-axis.

Description

SHAP summary plot for one feature with protected groups on y-axis.

Usage

h2o.fair_shap_plot(
  model,
  newdata,
  protected_columns,
  column,
  autoscale = TRUE,
  background_frame = NULL
)

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.
background_frame	Optional frame, that is used as the source of baselines for the marginal SHAP.

Value

list of ggplot2 objects

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_shap_plot(gbm, test, protected_columns, "AGE")

## End(Not run)

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