fairmodels (version 1.1.0)

fairness_radar: Fairness radar

Description

Make fairness_radar object with chosen fairness_metrics. Note that there must be at least three metrics that does not contain NA.

Usage

fairness_radar(x, fairness_metrics = c("ACC", "TPR", "PPV", "FPR", "STP"))

Arguments

x

object of class fairness_object

fairness_metrics

character, vector of metric names, at least 3 metrics without NA needed. Full names of metrics can be found in fairness_check documentation.

Value

fairness_radar object. It is a list containing:

• radar_data - data.frame containing scores for each model and parity loss metric

• label - model labels

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")

fradar <- fairness_radar(fobject, fairness_metrics = c("ACC", "STP", "TNR",
                                                       "TPR", "PPV"))

plot(fradar)

# }
# NOT RUN {
rf_model <- ranger::ranger(Risk ~.,
                           data = german,
                           probability = TRUE,
                           num.trees = 200,
                           num.threads = 1)


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

fobject <- fairness_check(explainer_rf, fobject)


fradar <- fairness_radar(fobject, fairness_metrics = c("ACC",
                                                       "STP",
                                                       "TNR",
                                                       "TPR",
                                                       "PPV"))

plot(fradar)
# }