Generates a website with HTML summaries for predictive models

modelDown(
  ...,
  modules = c("auditor", "drifter", "model_performance", "variable_importance",
    "variable_response"),
  output_folder = "output",
  repository_name = "repository",
  should_open_website = interactive()
)

Arguments

...	one or more explainers created with `DALEX::explain()` function. Pair of explainer could be provided to check drift of models
modules	modules that should be included in the website
output_folder	folder where the website will be saved
repository_name	name of local archivist repository that will be created
should_open_website	should generated website be automatically opened in default browser

Details

Additional arguments that could by passed by name:

remote_repository_path Path to remote repository that stores folder with archivist repository. If not provided, links to local repository will be shown.
device Device to use. Tested for "png" and "svg", but values from ggplot2::ggsave function should be working fine. Defaults to "png".
vr.vars variables which will be examined in Variable Response module. Defaults to all variables. Example vr.vars = c("var1", "var2")
vr.type types of examinations which will be conducteed in Variable Response module. Defaults to "partial". Example vr.type = c("partial", "conditional", "accumulated")

Author

Przemysław Biecek, Magda Tatarynowicz, Kamil Romaszko, Mateusz Urbański

Examples

if(FALSE){
require("ranger")
require("breakDown")
require("DALEX")


# Generate simple modelDown page
HR_data_selected <- HR_data[1000:3000,]
HR_glm_model <- glm(left~., HR_data_selected, family = "binomial")
explainer_glm <- explain(HR_glm_model, data=HR_data_selected, y = HR_data_selected$left)

modelDown::modelDown(explainer_glm,
                     modules = c("model_performance", "variable_importance",
                                 "variable_response"),
                     output_folder = tempdir(),
                     repository_name = "HR",
                     device = "png",
                     vr.vars= c("average_montly_hours"),
                     vr.type = "partial")

# More complex example with all modules
HR_ranger_model <- ranger(as.factor(left) ~ .,
                      data = HR_data, num.trees = 500, classification = TRUE, probability = TRUE)
explainer_ranger <- explain(HR_ranger_model,
                      data = HR_data, y = HR_data$left, function(model, data) {
 return(predict(model, data)$prediction[,2])
}, na.rm=TRUE)

# Two glm models used for drift detection
HR_data1 <- HR_data[1:4000,]
HR_data2 <- HR_data[4000:nrow(HR_data),]
HR_glm_model1 <- glm(left~., HR_data1, family = "binomial")
HR_glm_model2 <- glm(left~., HR_data2, family = "binomial")
explainer_glm1 <- explain(HR_glm_model1, data=HR_data1, y = HR_data1$left)
explainer_glm2 <- explain(HR_glm_model2, data=HR_data2, y = HR_data2$left)

modelDown::modelDown(list(explainer_glm1, explainer_glm2),
  modules = c("auditor", "drifter", "model_performance", "variable_importance",
              "variable_response"),
  output_folder = tempdir(),
  repository_name = "HR",
  remote_repository_path = "some_user/remote_repo_name",
  device = "png",
  vr.vars= c("average_montly_hours", "time_spend_company"),
  vr.type = "partial")
}