Print Individual Variable Effects

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

Arguments

x	an individual variable importance explainer created with the `individual_variable_effect` function.
...	further arguments passed to or from other methods.

Examples

have_shap <- reticulate::py_module_available("shap")

if(have_shap){
  library("shapper")
  library("DALEX")
  library("randomForest")
  Y_train <- HR$status
  x_train <- HR[ , -6]
  set.seed(123)
  model_rf <- randomForest(x = x_train, y = Y_train, ntree= 50)
  p_function <- function(model, data) predict(model, newdata = data, type = "prob")

  ive_rf <- individual_variable_effect(model_rf, data = x_train, predict_function = p_function,
                                     new_observation = x_train[1:2,], nsamples = 50)
  print(ive_rf)
}else{
    print('Python testing environment is required.')
}
#>     gender      age    hours evaluation salary _id_ _ylevel_ _yhat_ _yhat_mean_
#> 1     male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.3   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.4   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.5   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.6   male 32.58267 41.88626          3      1    1    fired    0.9   0.3787180
#> 1.1   male 32.58267 41.88626          3      1    1       ok    0.1   0.2730292
#>        _vname_ _attribution_ _sign_      _label_
#> 1       gender   -0.02056803      - randomForest
#> 1.3        age    0.03448379      + randomForest
#> 1.4      hours    0.32636349      + randomForest
#> 1.5 evaluation    0.11472954      + randomForest
#> 1.6     salary    0.06627323      + randomForest
#> 1.1     gender    0.01840784      + randomForest