Accumulated Local Effects Profiles accumulate local changes in Ceteris Paribus Profiles. Function accumulated_dependence calls ceteris_paribus and then aggregate_profiles.

accumulated_dependence(x, ...)

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

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

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

accumulated_dependency(x, ...)

Arguments

x

an explainer created with function DALEX::explain(), an object of the class ceteris_paribus_explainer 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 observations will be chosen randomly.

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 tocalculate_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 Accumulated Local Dependence Chapter.

References

ALEPlot: Accumulated Local Effects (ALE) Plots and Partial Dependence (PD) Plots https://cran.r-project.org/package=ALEPlot, Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

#> Welcome to DALEX (version: 2.3.0). #> Find examples and detailed introduction at: http://ema.drwhy.ai/ #> Additional features will be available after installation of: ggpubr. #> Use 'install_dependencies()' to get all suggested dependencies
#> #> Attaching package: ‘DALEX’
#> The following object is masked from ‘package:ingredients’: #> #> feature_importance
library("ingredients") 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) adp_glm <- accumulated_dependence(explain_titanic_glm, N = 25, variables = c("age", "fare")) head(adp_glm)
#> Top profiles : #> _vname_ _label_ _x_ _yhat_ _ids_ #> 1 age lm 0.1666667 0.000000000 0 #> 2 age lm 2.0000000 -0.002551359 0 #> 3 age lm 4.0000000 -0.005323191 0 #> 4 age lm 7.0000000 -0.009455409 0 #> 5 age lm 9.0000000 -0.012193262 0 #> 6 age lm 13.0000000 -0.017620564 0
plot(adp_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) adp_rf <- accumulated_dependence(explain_titanic_rf, N = 200, variable_type = "numerical") plot(adp_rf)
adp_rf <- accumulated_dependence(explain_titanic_rf, N = 200, variable_type = "categorical") plotD3(adp_rf, label_margin = 80, scale_plot = TRUE) # }