R/plotD3_rroc.R
plotD3_rroc.RdThe basic idea of the ROC curves for regression is to show model asymmetry. The RROC is a plot where on the x-axis we depict total over-estimation and on the y-axis total under-estimation.
plotD3_rroc(object, ..., scale_plot = FALSE)
| object | An object of class 'auditor_model_residual' created with |
|---|---|
| ... | 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. |
a `r2d3` object
For RROC curves we use a shift, which is an equivalent to the threshold for ROC curves. For each observation we calculate new prediction: \(\hat{y}'=\hat{y}+s\) where s is the shift. Therefore, there are different error values for each shift: \(e_i = \hat{y_i}' - y_i\)
Over-estimation is calculated as: \(OVER= \sum(e_i|e_i>0)\).
Under-estimation is calculated as: \(UNDER = \sum(e_i|e_i<0)\).
The shift equals 0 is represented by a dot.
The Area Over the RROC Curve (AOC) equals to the variance of the errors multiplied by \(frac{n^2}{2}\).
Hernández-Orallo, José. 2013. "ROC Curves for Regression". Pattern Recognition 46 (12): 3395–3411.
plotD3_rroc
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$life_length)#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 100 rows 8 cols #> -> target variable : 100 values #> -> predict function : yhat.lm will be used ( default ) #> -> predicted values : No value for predict function target column. ( default ) #> -> model_info : package stats , ver. 4.1.1 , task regression ( default ) #> -> predicted values : numerical, min = 585.8311 , mean = 1347.787 , max = 2942.307 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -88.41755 , mean = -1.489291e-13 , max = 77.92805 #> A new explainer has been created!# validate a model with auditor mr_lm <- model_residual(lm_audit) # plot results plotD3_rroc(mr_lm) library(randomForest) model_rf <- randomForest(life_length~., data = dragons) rf_audit <- audit(model_rf, data = dragons, y = dragons$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 = 767.3926 , mean = 1341.1 , max = 2461.078 #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -191.8798 , mean = 6.687034 , max = 437.3425 #> A new explainer has been created!