Measure performance in both fairness metric and
performance_and_fairness(x, fairness_metric = NULL, performance_metric = NULL)
object of class
fairness metric, one of metrics in fairness_objects parity_loss_metric_data (ACC, TPR, PPV, ...) Full list in
performance metric, one of
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.
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
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) 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)
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