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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