# Dataset Level Variable Importance as Change in Loss Function after Variable Permutations From DALEX version 1.0 this function calls the `feature_importance` Find information how to use this function here: [https://ema.drwhy.ai/featureImportance.html](https://ema.drwhy.ai/featureImportance.html). ```r model_parts( explainer, loss_function = loss_default(explainer$model_info$type), ..., type = "variable_importance", N = n_sample, n_sample = 1000 ) ``` ## Arguments - `explainer`: a model to be explained, preprocessed by the `explain` function - `loss_function`: a function that will be used to assess variable importance. By default it is 1-AUC for classification, cross entropy for multilabel classification and RMSE for regression. Custom, user-made loss function should accept two obligatory parameters (observed, predicted), where `observed` states for actual values of the target, while `predicted` for predicted values. If attribute "loss_accuracy" is associated with function object, then it will be plotted as name of the loss function. - `...`: other parameters - `type`: character, type of transformation that should be applied for dropout loss. `variable_importance` and `raw` results raw drop lossess, `ratio` returns `drop_loss/drop_loss_full_model` while `difference` returns `drop_loss - drop_loss_full_model` - `N`: number of observations that should be sampled for calculation of variable importance. If `NULL` then variable importance will be calculated on whole dataset (no sampling). - `n_sample`: alias for `N` held for backwards compatibility. number of observations that should be sampled for calculation of variable importance. ## Returns An object of the class `feature_importance`. It's a data frame with calculated average response. ## Examples ```r # regression library("ranger") apartments_ranger_model <- ranger(m2.price~., data = apartments, num.trees = 50) explainer_ranger <- explain(apartments_ranger_model, data = apartments[,-1], y = apartments$m2.price, label = "Ranger Apartments") model_parts_ranger_aps <- model_parts(explainer_ranger, type = "raw") head(model_parts_ranger_aps, 8) plot(model_parts_ranger_aps) # binary classification titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial") explainer_glm_titanic <- explain(titanic_glm_model, data = titanic_imputed[,-8], y = titanic_imputed$survived) logit <- function(x) exp(x)/(1+exp(x)) custom_loss <- function(observed, predicted){ sum((observed - logit(predicted))^2) } attr(custom_loss, "loss_name") <- "Logit residuals" model_parts_glm_titanic <- model_parts(explainer_glm_titanic, type = "raw", loss_function = custom_loss) head(model_parts_glm_titanic, 8) plot(model_parts_glm_titanic) # multilabel classification HR_ranger_model_HR <- ranger(status~., data = HR, num.trees = 50, probability = TRUE) explainer_ranger_HR <- explain(HR_ranger_model_HR, data = HR[,-6], y = HR$status, label = "Ranger HR") model_parts_ranger_HR <- model_parts(explainer_ranger_HR, type = "raw") head(model_parts_ranger_HR, 8) plot(model_parts_ranger_HR) ``` ## References Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. [https://ema.drwhy.ai/](https://ema.drwhy.ai/)

model_parts function