Produce plots and dataframes related to a single model fairness.

Usage

h2o.inspect_model_fairness(
  model,
  newdata,
  protected_columns,
  reference,
  favorable_class,
  metrics = c("auc", "aucpr", "f1", "p.value", "selectedRatio", "total"),
  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.
reference: List of values corresponding to a reference for each protected columns. If set to NULL, it will use the biggest group as the reference.
favorable_class: Positive/favorable outcome class of the response.
metrics: Character vector of metrics to show.
background_frame: Optional frame, that is used as the source of baselines for the marginal SHAP. Setting it enables calculating SHAP in more models but it can be more time and memory consuming.

Value

H2OExplanation object

Examples

if (FALSE) { # \dontrun{
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.inspect_model_fairness(gbm, test, protected_columns = protected_columns,
                           reference = reference, favorable_class = favorable_class)
} # }