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.

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.

Value

a r2d3 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) lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.lm will be used ( default ) #> -> predicted values : No value for predict function target column. ( default ) #> -> model_info : package stats , ver. 4.1.1 , task regression ( default ) #> -> predicted values : numerical, min = 585.8311 , mean = 1347.787 , max = 2942.307 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -88.41755 , mean = -1.489291e-13 , max = 77.92805 #> A new explainer has been created!
# validate a model with auditor mr_lm <- model_residual(lm_audit) plotD3_rec(mr_lm) library(randomForest) model_rf <- randomForest(life_length~., data = dragons) rf_audit <- audit(model_rf, data = dragons, y = dragons$life_length)
#> 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 : No value for predict function target column. ( default ) #> -> model_info : package randomForest , ver. 4.6.14 , task regression ( default ) #> -> predicted values : numerical, min = 747.1454 , mean = 1346.67 , max = 2464.744 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -209.3837 , mean = 1.116552 , max = 433.6763 #> A new explainer has been created!
mr_rf <- model_residual(rf_audit) plotD3_rec(mr_lm, mr_rf)