Merge the observations of modelStudio objects

This function merges local explanations from multiple modelStudio objects into one.

ms_merge_observations(...)

Arguments

...

modelStudio objects created with modelStudio().

Value

An object of the r2d3, htmlwidget, modelStudio class.

References

• The input object is implemented in DALEX

• Feature Importance, Ceteris Paribus, Partial Dependence and Accumulated Dependence explanations are implemented in ingredients

• Break Down and Shapley Values explanations are implemented in iBreakDown

See also

Vignettes: modelStudio - R & Python examples and modelStudio - perks and features

Examples

# \donttest{
library("DALEX")
library("modelStudio")

# fit a model
model_happiness <- glm(score ~., data = happiness_train)

# create an explainer for the model
explainer_happiness <- explain(model_happiness,
                               data = happiness_test,
                               y = happiness_test$score)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  156  rows  7  cols 
#>   -> target variable   :  156  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.3.1 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  2.726257 , mean =  5.553508 , max =  7.560368  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -1.976856 , mean =  -0.1464115 , max =  0.9116971  
#>   A new explainer has been created!  

# make studios for the model
ms1 <- modelStudio(explainer_happiness,
                   N = 200,  B = 5)
#> `new_observation` argument is NULL. `new_observation_n` observations needed to calculate local explanations are taken from the data.

ms2 <- modelStudio(explainer_happiness,
                   new_observation = head(happiness_test, 3),
                   N = 200,  B = 5)

# merge 
ms <- ms_merge_observations(ms1, ms2)
ms

# }