# Dataset Level Variable Profile as Partial Dependence or Accumulated Local Dependence Explanations This function calculates explanations on a dataset level set that explore model response as a function of selected variables. The explanations can be calulated as Partial Dependence Profile or Accumulated Local Dependence Profile. Find information how to use this function here: [https://ema.drwhy.ai/partialDependenceProfiles.html](https://ema.drwhy.ai/partialDependenceProfiles.html). The `variable_profile` function is a copy of `model_profile`. ```r model_profile( explainer, variables = NULL, N = 100, ..., groups = NULL, k = NULL, center = TRUE, type = "partial" ) variable_profile( explainer, variables = NULL, N = 100, ..., groups = NULL, k = NULL, center = TRUE, type = "partial" ) single_variable(explainer, variable, type = "pdp", ...) ``` ## Arguments - `explainer`: a model to be explained, preprocessed by the `explain` function - `variables`: character - names of variables to be explained - `N`: number of observations used for calculation of aggregated profiles. By default `100`. Use `NULL` to use all observations. - `...`: other parameters that will be passed to `ingredients::aggregate_profiles` - `groups`: a variable name that will be used for grouping. By default `NULL` which means that no groups shall be calculated - `k`: number of clusters for the hclust function (for clustered profiles) - `center`: shall profiles be centered before clustering - `type`: the type of variable profile. Either `partial`, `conditional` or `accumulated`. - `variable`: deprecated, use variables instead ## Returns An object of the class `model_profile`. It's a data frame with calculated average model responses. ## Details Underneath this function calls the `partial_dependence` or `accumulated_dependence` functions from the `ingredients` package. ## Examples ```r titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial") explainer_glm <- explain(titanic_glm_model, data = titanic_imputed) model_profile_glm_fare <- model_profile(explainer_glm, "fare") plot(model_profile_glm_fare) library("ranger") titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50, probability = TRUE) explainer_ranger <- explain(titanic_ranger_model, data = titanic_imputed) model_profile_ranger <- model_profile(explainer_ranger) plot(model_profile_ranger, geom = "profiles") model_profile_ranger_1 <- model_profile(explainer_ranger, type = "partial", variables = c("age", "fare")) plot(model_profile_ranger_1 , variables = c("age", "fare"), geom = "points") model_profile_ranger_2 <- model_profile(explainer_ranger, type = "partial", k = 3) plot(model_profile_ranger_2 , geom = "profiles") model_profile_ranger_3 <- model_profile(explainer_ranger, type = "partial", groups = "gender") plot(model_profile_ranger_3 , geom = "profiles") model_profile_ranger_4 <- model_profile(explainer_ranger, type = "accumulated") plot(model_profile_ranger_4 , geom = "profiles") # Multiple profiles model_profile_ranger_fare <- model_profile(explainer_ranger, "fare") plot(model_profile_ranger_fare, model_profile_glm_fare) ``` ## References Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. [https://ema.drwhy.ai/](https://ema.drwhy.ai/)

model_profile function