h2o (version 3.40.0.4)

h2o.fair_shap_plot: 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)

Value

list of ggplot2 objects

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