fairmodels (version 1.1.0)

performance_and_fairness: Performance and fairness

Description

Measure performance in both fairness metric and

Usage

performance_and_fairness(x, fairness_metric = NULL, performance_metric = NULL)

Arguments

x

object of class fairness_object

fairness_metric

fairness metric, one of metrics in fairness_objects parity_loss_metric_data (ACC, TPR, PPV, ...) Full list in fairness_check documentation.

performance_metric

performance metric, one of

Value

performance_and_fairness object. It is list containing:

• paf_data - performance and fairness data.frame containing fairness and performance metric scores for each model

• fairness_metric - chosen fairness metric name

• performance_metric - chosen performance_metric name

• label - model labels

Details

Creates perfomance_and_fairness object. Measure model performance and model fairness metric at the same time. Choose best model according to both metrics. When plotted y axis is inversed to accentuate that models in top right corner are the best according to both metrics.

Examples

# NOT RUN {
data("german")

y_numeric <- as.numeric(german$Risk) -1

lm_model <- glm(Risk~.,
                data = german,
                family=binomial(link="logit"))


explainer_lm <- DALEX::explain(lm_model, data = german[,-1], y = y_numeric)

fobject <- fairness_check(explainer_lm,
                          protected = german$Sex,
                          privileged = "male")

paf <- performance_and_fairness(fobject)
plot(paf)

# }
# NOT RUN {
rf_model <- ranger::ranger(Risk ~.,
                           data = german,
                           probability = TRUE,
                           num.trees = 200)

explainer_rf <- DALEX::explain(rf_model, data = german[,-1], y = y_numeric)

fobject <- fairness_check(explainer_rf, fobject)

 # same explainers with different cutoffs for female
fobject <- fairness_check(explainer_lm, explainer_rf, fobject,
                          protected = german$Sex,
                          privileged = "male",
                          cutoff = list( female = 0.4),
                          label = c("lm_2", "rf_2"))

paf <- performance_and_fairness(fobject)

plot(paf)

# }