Partial Dependence Profiles

Partial Dependence Profiles are averages from Ceteris Paribus Profiles. Function partial_dependence calls ceteris_paribus and then aggregate_profiles.

partial_dependence(x, ...)

# S3 method for explainer
partial_dependence(
  x,
  variables = NULL,
  N = 500,
  variable_splits = NULL,
  grid_points = 101,
  ...,
  variable_type = "numerical"
)

# S3 method for default
partial_dependence(
  x,
  data,
  predict_function = predict,
  label = class(x)[1],
  variables = NULL,
  grid_points = 101,
  variable_splits = NULL,
  N = 500,
  ...,
  variable_type = "numerical"
)

# S3 method for ceteris_paribus_explainer
partial_dependence(x, ..., variables = NULL)

partial_dependency(x, ...)

Arguments

x	an explainer created with function DALEX::explain(), an object of the class ceteris_paribus_explainer or or a model to be explained.
...	other parameters
variables	names of variables for which profiles shall be calculated. Will be passed to calculate_variable_split. If NULL then all variables from the validation data will be used.
N	number of observations used for calculation of partial dependence profiles. By default 500.
variable_splits	named list of splits for variables, in most cases created with calculate_variable_split. If NULL then it will be calculated based on validation data avaliable in the explainer.
grid_points	number of points for profile. Will be passed to calculate_variable_split.
variable_type	a character. If numerical then only numerical variables will be calculated. If categorical then only categorical variables will be calculated.
data	validation dataset, will be extracted from x if it's an explainer NOTE: It is best when target variable is not present in the data
predict_function	predict function, will be extracted from x if it's an explainer
label	name of the model. By default it's extracted from the class attribute of the model

Value

an object of the class aggregated_profiles_explainer

Details

Find more detailes in the Partial Dependence Profiles Chapter.

References

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://pbiecek.github.io/ema

Examples

library("DALEX")

model_titanic_glm <- glm(survived ~ gender + age + fare,
                         data = titanic_imputed, family = "binomial")

explain_titanic_glm <- explain(model_titanic_glm,
                               data = titanic_imputed[,-8],
                               y = titanic_imputed[,8],
                               verbose = FALSE)

pdp_glm <- partial_dependence(explain_titanic_glm,
                              N = 25, variables = c("age", "fare"))
head(pdp_glm)
#> Top profiles    : 
#>   _vname_ _label_       _x_    _yhat_ _ids_
#> 1    fare      lm 0.0000000 0.2634803     0
#> 2     age      lm 0.1666667 0.3183096     0
#> 3     age      lm 2.0000000 0.3158061     0
#> 4     age      lm 4.0000000 0.3130895     0
#> 5    fare      lm 6.1793080 0.2709197     0
#> 6     age      lm 7.0000000 0.3090432     0
plot(pdp_glm)

# \donttest{
library("randomForest")

model_titanic_rf <- randomForest(survived ~.,  data = titanic_imputed)
#> Warning: The response has five or fewer unique values.  Are you sure you want to do regression?

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed[,8],
                              verbose = FALSE)

pdp_rf <- partial_dependence(explain_titanic_rf, variable_type = "numerical")
plot(pdp_rf)

pdp_rf <- partial_dependence(explain_titanic_rf, variable_type = "categorical")
plotD3(pdp_rf, label_margin = 80, scale_plot = TRUE)
# }