plotD3_roc: Receiver Operating Characteristic (ROC) in D3 with r2d3...

In auditor: Model Audit - Verification, Validation, and Error Analysis

Receiver Operating Characteristic (ROC) in D3 with r2d3 package.

Description

Receiver Operating Characteristic Curve is a plot of the true positive rate (TPR) against the false positive rate (FPR) for the different thresholds. It is useful for measuring and comparing the accuracy of the classificators.

Usage

plotD3_roc(object, ..., nlabel = NULL, scale_plot = FALSE)

Arguments

object	An object of class auditor_model_evaluation created with model_evaluation function.
...	Other auditor_model_evaluation objects to be plotted together.
nlabel	Number of cutoff points to show on the plot. Default is NULL.
scale_plot	Logical, indicates whenever the plot should scale with height. By default it's FALSE.

Value

a r2d3 object

See Also

plot_roc

Examples

data(titanic_imputed, package = "DALEX")

# fit a model
model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)

# use DALEX package to wrap up a model into explainer
glm_audit <- audit(model_glm,
                   data = titanic_imputed,
                   y = titanic_imputed$survived)

# validate a model with auditor
eva_glm <- model_evaluation(glm_audit)

# plot results
plot_roc(eva_glm)
plot(eva_glm)

#add second model
model_glm_2 <- glm(survived ~ .-age, family = binomial, data = titanic_imputed)
glm_audit_2 <- audit(model_glm_2,
                     data = titanic_imputed,
                     y = titanic_imputed$survived,
                     label = "glm2")
eva_glm_2 <- model_evaluation(glm_audit_2)

plotD3_roc(eva_glm, eva_glm_2)

auditor documentation built on Nov. 2, 2023, 6:13 p.m.