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.

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.

Value

A ggplot object.

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

References

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

See also

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 exp_lm <- DALEX::explain(model_lm, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated #> -> model label : lm (default) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.lm will be used (default) #> -> predicted values : numerical, min = 585.8311 , mean = 1347.787 , max = 2942.307 #> -> residual function : difference between y and yhat (default) #> -> residuals : numerical, min = -88.41755 , mean = -1.489291e-13 , max = 77.92805 #> A new explainer has been created!
# validate a model with auditor library(auditor) mr_lm <- model_residual(exp_lm) plot_rec(mr_lm)
plot(mr_lm, type = "rec")
library(randomForest) model_rf <- randomForest(life_length~., data = dragons) exp_rf <- DALEX::explain(model_rf, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated #> -> model label : randomForest (default) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.randomForest will be used (default) #> -> predicted values : numerical, min = 770.8553 , mean = 1343.446 , max = 2465.784 #> -> residual function : difference between y and yhat (default) #> -> residuals : numerical, min = -207.8857 , mean = 4.341221 , max = 432.6363 #> A new explainer has been created!
mr_rf <- model_residual(exp_rf) plot_rec(mr_lm, mr_rf)
plot(mr_lm, mr_rf, type = "rec")