Predicted response vs Observed or Variable Values

Plot of predicted response vs observed or variable Values.

plot_prediction(object, ..., variable = "_y_", smooth = FALSE, abline = FALSE)

plotPrediction(object, ..., variable = NULL, smooth = FALSE, abline = FALSE)

Arguments

object	An object of class auditor_model_residual.
...	Other auditor_model_residual objects to be plotted together.
variable	Name of variable to order residuals on a plot. If variable="_y_", the data is ordered by a vector of actual response (y parameter passed to the explain function). If variable = "_y_hat_" the data on the plot will be ordered by predicted response. If variable = NULL, unordered observations are presented.
smooth	Logical, indicates whenever smooth line should be added.
abline	Logical, indicates whenever function y = x should be added. Works only with variable = "_y_" (which is a default option) or when variable equals actual response variable.

Value

A ggplot2 object.

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)

# plot results
plot_prediction(mr_lm, abline = TRUE)
plot_prediction(mr_lm, variable = "height", smooth = TRUE)
plot(mr_lm, type = "prediction", abline = TRUE)

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 =  766.2776 , mean =  1342.534 , max =  2470.308  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -185.7102 , mean =  5.252424 , max =  428.1124  
#>   A new explainer has been created!  
mr_rf <- model_residual(rf_audit)
plot_prediction(mr_lm, mr_rf, variable = "height", smooth = TRUE)