R/plotD3_rec.R
plotD3_rec.RdError 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)
| 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
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
Bi J., Bennett K.P. (2003). Regression error characteristic curves, in: Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC.
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!