# Create a frame containing aggregations of intersectional fairness across the models. ```r h2o.disparate_analysis( models, newdata, protected_columns, reference, favorable_class, air_metric = "selectedRatio", alpha = 0.05 ) ``` ## Arguments - `models`: List of H2O Models - `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. - `air_metric`: Metric used for Adverse Impact Ratio calculation. Defaults to ``selectedRatio``. - `alpha`: The alpha level is the probability of rejecting the null hypothesis that the protected group and the reference came from the same population when the null hypothesis is true. ## Returns frame containing aggregations of intersectional fairness across the models ## Examples ```r ## 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 aml <- h2o.automl(x, y, training_frame = train, max_models = 3) h2o.disparate_analysis(aml, test, protected_columns = protected_columns, reference = reference, favorable_class = favorable_class) ## End(Not run) ```

h2o.disparate_analysis function