fairness_heatmap object to compare both models and metrics.
scale is set to
TRUE metrics will be scaled to median = 0 and sd = 1.
If NA's appear heatmap will still plot, but with gray area where NA's were.
fairness_heatmap(x, scale = FALSE)
object of class
logical, if codeTRUE metrics will be scaled to mean 0 and sd 1. Default
It is a list with following fields:
data.frame with information about score for model and parity loss metric
matrix_model - matrix used in dendogram plots
scale - logical parameter passed to
label - character, vector of model labels
data("german") y_numeric <- as.numeric(german$Risk) - 1 lm_model <- glm(Risk ~ ., data = german, family = binomial(link = "logit") ) rf_model <- ranger::ranger(Risk ~ ., data = german, probability = TRUE, num.trees = 200, num.threads = 1 ) explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric) explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric) fobject <- fairness_check(explainer_lm, explainer_rf, protected = german$Sex, privileged = "male" ) # 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") ) fh <- fairness_heatmap(fobject) plot(fh)
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