ingredients (version 2.3.0)

partial_dependence: Partial Dependence Profiles

Description

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

Usage

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, ...)

Value

an object of the class aggregated_profiles_explainer

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

Details

Find more details in the Partial Dependence Profiles Chapter.

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

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)
plot(pdp_glm)

# \donttest{
library("ranger")

model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE)

explain_titanic_rf <- explain(model_titanic_rf,
                              data = titanic_imputed[,-8],
                              y = titanic_imputed[,8],
                              label = "ranger forest",
                              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)
# }