fairmodels (version 1.2.1)

metric_scores: Metric scores

Description

Creates metric_scores object to facilitate visualization. Check how the metric scores differ among models, what is this score, and how it changes for example after applying bias mitigation technique. The vertical black lines denote the scores for privileged subgroup. It is best to use only few metrics (using fairness_metrics parameter)

Usage

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

Value

metric_scores object. It is a list containing:

• metric_scores_data - data.frame with information about score in particular subgroup, metric, and model

• privileged - name of privileged subgroup

Arguments

x

object of class fairness_object

fairness_metrics

character, vector with fairness metric names. Default metrics are ones in fairness_check plot, full names can be found in fairness_check documentation.

Examples

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"
)

ms <- metric_scores(fobject, fairness_metrics = c("ACC", "TPR", "PPV", "FPR", "STP"))
plot(ms)
# \donttest{

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)

ms <- metric_scores(fobject, fairness_metrics = c("ACC", "TPR", "PPV", "FPR", "STP"))
plot(ms)
# }