Print Dataset Level Model Performance Summary

# S3 method for model_performance
print(x, ...)

Arguments

x: a model to be explained, object of the class 'model_performance_explainer'
...: other parameters

Examples

 # \donttest{
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 100,
                               probability = TRUE)
# It's a good practice to pass data without target variable
explainer_ranger  <- explain(titanic_ranger_model, data = titanic_imputed[,-8],
                             y = titanic_imputed$survived)
#> Preparation of a new explainer is initiated
#>   -> model label       :  ranger  (  default  )
#>   -> data              :  2207  rows  7  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.ranger  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package ranger , ver. 0.14.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.008182132 , mean =  0.3222051 , max =  0.9901092  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.7894434 , mean =  -4.831517e-05 , max =  0.8808497  
#>   A new explainer has been created!  
# resulting dataframe has predicted values and residuals
mp_ex_rn <- model_performance(explainer_ranger)
mp_ex_rn
#> Measures for:  classification
#> recall     : 0.5738397 
#> precision  : 0.8947368 
#> f1         : 0.6992288 
#> accuracy   : 0.8409606 
#> auc        : 0.9007602
#> 
#> Residuals:
#>          0%         10%         20%         30%         40%         50% 
#> -0.78944337 -0.25433067 -0.20607331 -0.18331391 -0.15959006 -0.13094821 
#>         60%         70%         80%         90%        100% 
#> -0.09476201  0.03200369  0.24348788  0.60113328  0.88084970 
plot(mp_ex_rn)

 # }