ceterisParibus (version 0.4.2)

ceteris_paribus: Ceteris Paribus Explainer

Description

This function calculate ceteris paribus profiles for selected data points.

Usage

ceteris_paribus(
  explainer,
  observations,
  y = NULL,
  variable_splits = NULL,
  variables = NULL,
  grid_points = 101
)

Arguments

explainer

a model to be explained, preprocessed by function `DALEX::explain()`.

observations

set of observarvation for which profiles are to be calculated

y

true labels for `observations`. If specified then will be added to ceteris paribus plots.

variable_splits

named list of splits for variables, in most cases created with `calculate_variable_splits()`. If NULL then it will be calculated based on validation data avaliable in the `explainer`.

variables

names of variables for which profiles shall be calculated. Will be passed to `calculate_variable_splits()`. If NULL then all variables from the validation data will be used.

grid_points

number of points for profile. Will be passed to `calculate_variable_splits()`.

Value

An object of the class 'ceteris_paribus_explainer'. It's a data frame with calculated average responses.

Examples

# NOT RUN {
library("DALEX")
 
# }
# NOT RUN {
library("randomForest")
set.seed(59)

apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
      no.rooms + district, data = apartments)

explainer_rf <- explain(apartments_rf_model,
      data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)

apartments_small <- select_sample(apartmentsTest, 10)

cp_rf <- ceteris_paribus(explainer_rf, apartments_small)
cp_rf

cp_rf <- ceteris_paribus(explainer_rf, apartments_small, y = apartments_small$m2.price)
cp_rf
# }