plot_rec: Regression Error Characteristic Curves (REC)

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

Regression Error Characteristic Curves (REC)

Description

Error Characteristic curves are a generalization of ROC curves. On the x axis of the plot there is an error tolerance and on the y axis there is a percentage of observations predicted within the given tolerance.

Usage

plot_rec(object, ...)

plotREC(object, ...)

Arguments

object	An object of class auditor_model_residual created with model_residual function.
...	Other auditor_model_residual objects to be plotted together.

Details

REC curve estimates the Cumulative Distribution Function (CDF) of the error

Area Over the REC Curve (REC) is a biased estimate of the expected error

Value

A ggplot object.

References

Bi J., Bennett K.P. (2003). Regression error characteristic curves, in: Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC.

See Also

plot_roc, plot_rroc

Examples

dragons <- DALEX::dragons[1:100, ]

# fit a model
model_lm <- lm(life_length ~ ., data = dragons)

# use DALEX package to wrap up a model into explainer
lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length)

# validate a model with auditor
mr_lm <- model_residual(lm_audit)
plot_rec(mr_lm)
plot(mr_lm, type = "rec")

library(randomForest)
model_rf <- randomForest(life_length~., data = dragons)
rf_audit <- audit(model_rf, data = dragons, y = dragons$life_length)
mr_rf <- model_residual(rf_audit)
plot_rec(mr_lm, mr_rf)
plot(mr_lm, mr_rf, type = "rec")

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