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.
Returns
H2OExplanation 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 manfavorable_class <-"0"# no default next monthgbm <- h2o.gbm(x, y, training_frame = train)h2o.inspect_model_fairness(gbm, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class)## End(Not run)