plot_prc {auditor}

Precision-Recall Curve (PRC)

Description

Precision-Recall Curve summarize the trade-off between the true positive rate and the positive predictive value for a model. It is useful for measuring performance and comparing classificators.

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

plot_prc(object, ..., nlabel = NULL)

plot_roc(object, ..., nlabel = NULL)

plotROC(object, ..., nlabel = NULL)

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.

Value

A ggplot object.

A ggplot object.

See Also

plot_rroc, plot_rec

Examples

library(DALEX)

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

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_prc(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)

plot_prc(eva_glm, eva_glm_2)
plot(eva_glm, eva_glm_2)

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)

plot_roc(eva_glm, eva_glm_2)
plot(eva_glm, eva_glm_2)