plotD3_rec: Regression Error Characteristic Curves (REC) in D3 with r2d3...

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

Regression Error Characteristic Curves (REC) in D3 with r2d3 package.

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

plotD3_rec(object, ..., scale_plot = FALSE)

plotD3REC(object, ..., scale_plot = FALSE)

Arguments

object	An object of class 'auditor_model_residual' created with model_residual function.
...	Other 'auditor_model_residual' objects to be plotted together.
scale_plot	Logical, indicates whenever the plot should scale with height. By default it's FALSE.

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 r2d3 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_rec

Examples

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

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

lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length)

# validate a model with auditor
mr_lm <- model_residual(lm_audit)
plotD3_rec(mr_lm)

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)
plotD3_rec(mr_lm, mr_rf)

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