Creating model explainer

library("randomForest")
library("DALEX")

explain_rf <- DALEX::explain(model = titanic_rf,  
                             data = titanic_imputed[, -9],
                             y = titanic_imputed$survived == "yes", 
                             label = "Random Forest")

## Preparation of a new explainer is initiated
##   -> model label       :  Random Forest 
##   -> data              :  2207  rows  8  cols 
##   -> target variable   :  2207  values 
##   -> predict function  :  yhat.randomForest  will be used (  default  )
##   -> predicted values  :  No value for predict function target column. (  default  )
##   -> model_info        :  package randomForest , ver. 4.7.1.1 , task classification (  default  ) 
##   -> model_info        :  Model info detected classification task but 'y' is a logical . Converted to numeric.  (  NOTE  )
##   -> predicted values  :  numerical, min =  0 , mean =  0.2353095 , max =  1  
##   -> residual function :  difference between y and yhat (  default  )
##   -> residuals         :  numerical, min =  -0.892 , mean =  0.0868473 , max =  1  
##   A new explainer has been created!

predict(explain_rf, henry)

## [1] 0.246