model_profile function

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. The variable_profile function is a copy of model_profile.

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

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/