Print ceteris paribus cutoff
# S3 method for ceteris_paribus_cutoff
print(x, ...)| x |
|
|---|---|
| ... | other print parameters |
data("german")
german <- german[1:500, ]
y_numeric <- as.numeric(german$Risk) - 1
lm_model <- glm(Risk ~ .,
data = german,
family = binomial(link = "logit")
)
rf_model <- ranger::ranger(Risk ~ .,
data = german,
probability = TRUE,
num.trees = 200,
num.threads = 1
)
explainer_lm <- DALEX::explain(lm_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#> -> model label : lm ( default )
#> -> data : 500 rows 9 cols
#> -> target variable : 500 values
#> -> predict function : yhat.glm will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package stats , ver. 4.1.1 , task classification ( default )
#> -> predicted values : numerical, min = 0.1320977 , mean = 0.728 , max = 0.9836791
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.952954 , mean = 1.222888e-13 , max = 0.7735841
#> A new explainer has been created!
explainer_rf <- DALEX::explain(rf_model, data = german[, -1], y = y_numeric)
#> Preparation of a new explainer is initiated
#> -> model label : ranger ( default )
#> -> data : 500 rows 9 cols
#> -> target variable : 500 values
#> -> predict function : yhat.ranger will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package ranger , ver. 0.13.1 , task classification ( default )
#> -> predicted values : numerical, min = 0.09960516 , mean = 0.7264954 , max = 0.9966667
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.7163019 , mean = 0.001504627 , max = 0.5682619
#> A new explainer has been created!
fobject <- fairness_check(explainer_lm, explainer_rf,
protected = german$Sex,
privileged = "male"
)
#> Creating fairness classification object
#> -> Privileged subgroup : character ( Ok )
#> -> Protected variable : factor ( Ok )
#> -> Cutoff values for explainers : 0.5 ( for all subgroups )
#> -> Fairness objects : 0 objects
#> -> Checking explainers : 2 in total ( compatible )
#> -> Metric calculation : 13/13 metrics calculated for all models
#> Fairness object created succesfully
ceteris_paribus_cutoff(fobject, "female")
#>
#> Ceteribus paribus cutoff for subgroup: female
#>
#> First rows from data:
#> parity_loss metric cutoff model
#> 1 0.07972347 TPR 0.00 lm
#> 2 0.17350489 PPV 0.00 lm
#> 3 0.40546511 FPR 0.00 lm
#> 4 0.15175891 STP 0.00 lm
#> 5 0.13537101 ACC 0.00 lm
#> 6 0.07972347 TPR 0.01 lm
#>
#> Minimums:
#> model mins
#> lm lm 0.39
#> ranger ranger 0.50
#>