View source: R/model_variable_effect.R
| variable_effect | R Documentation | 
From DALEX version 1.0 this function calls the accumulated_dependence or
partial_dependence from the ingredients package.
Find information how to use this function here: https://ema.drwhy.ai/partialDependenceProfiles.html.
variable_effect(explainer, variables, ..., type = "partial_dependency")
variable_effect_partial_dependency(explainer, variables, ...)
variable_effect_accumulated_dependency(explainer, variables, ...)
| explainer | a model to be explained, preprocessed by the 'explain' function | 
| variables | character - names of variables to be explained | 
| ... | other parameters | 
| type | character - type of the response to be calculated. Currently following options are implemented: 'partial_dependency' for Partial Dependency and 'accumulated_dependency' for Accumulated Local Effects | 
An object of the class 'aggregated_profiles_explainer'. It's a data frame with calculated average response.
Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/
titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial")
explainer_glm <- explain(titanic_glm_model, data = titanic_imputed)
expl_glm <- variable_effect(explainer_glm, "fare", "partial_dependency")
plot(expl_glm)
 
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50,
                               probability = TRUE)
explainer_ranger  <- explain(titanic_ranger_model, data = titanic_imputed)
expl_ranger  <- variable_effect(explainer_ranger, variables = "fare",
                            type = "partial_dependency")
plot(expl_ranger)
plot(expl_ranger, expl_glm)
# Example for factor variable (with factorMerger)
expl_ranger_factor  <- variable_effect(explainer_ranger, variables =  "class")
plot(expl_ranger_factor)
 
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