print_fairness_pca: Print fairness PCA

Description Usage Arguments Examples

Description

Print principal components after using pca on fairness object

Usage

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## S3 method for class 'fairness_pca'
print(x, ...)

Arguments

x

fairness_pca object

...

other print parameters

Examples

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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"))

fpca <- fairness_pca(fobject)

print(fpca)

fairmodels documentation built on May 31, 2021, 5:07 p.m.