fairness (version 1.2.0)

roc_parity: ROC AUC parity

Description

This function computes the ROC AUC parity metric

Usage

roc_parity(
  data,
  outcome,
  group,
  probs,
  preds_levels = NULL,
  base = NULL,
  group_breaks = NULL
)

Arguments

data

The dataframe that contains the necessary columns.

outcome

The column name of the actual outcomes.

group

Sensitive group to examine.

probs

The column name or vector of the predicted probabilities (numeric between 0 - 1).

preds_levels

The desired levels of the predicted binary outcome. If not defined, levels of the outcome variable are used.

base

Base level for sensitive group comparison.

group_breaks

If group is continuous (e.g., age): either a numeric vector of two or more unique cut points or a single number >= 2 giving the number of intervals into which group feature is to be cut.

Value

Metric

Raw ROC AUC metrics for all groups and metrics standardized for the base group (parity metric). Lower values compared to the reference group mean lower ROC AUC values in the selected subgroups

Metric_plot

Bar plot of ROC AUC metric

Probability_plot

Density plot of predicted probabilities per subgroup

ROCAUC_plot

ROC plots for all subgroups

Details

This function computes the ROC AUC values for each subgroup. In the returned table, the reference group will be assigned 1, while all other groups will be assigned values according to whether their ROC AUC values are lower or higher compared to the reference group. Lower ROC AUC will be reflected in numbers lower than 1 in the returned named vector, thus numbers lower than 1 mean WORSE prediction for the subgroup.

Examples

# NOT RUN {
data(compas)
roc_parity(data = compas, outcome = 'Two_yr_Recidivism', group = 'ethnicity',
probs = 'probability', base = 'Caucasian')
roc_parity(data = compas, outcome = 'Two_yr_Recidivism', group = 'ethnicity',
probs = 'probability', base = 'African_American')

# }