individual_conditional_expectations: Local Conditional Expectation Explainer

In pbiecek/ceterisParibus2: Ceteris Paribus Profiles

Description

This explainer works for individual observations. For each observation it calculates Local Conditional Expectation (LCE) profiles for selected variables.

Usage

individual_conditional_expectations(x, ...)

## S3 method for class 'explainer'
individual_conditional_expectations(x, new_observation,
  y = NULL, variables = NULL, variable_splits = NULL,
  grid_points = 101, ...)

## Default S3 method:
individual_conditional_expectations(x, data,
  predict_function = predict, new_observation, y = NULL,
  variable_splits = NULL, variables = NULL, grid_points = 101,
  label = class(x)[1], ...)

Arguments

x	a model to be explained, or an explainer created with function 'DALEX::explain()'.
...	other parameters
new_observation	a new observation with columns that corresponds to variables used in the model
y	true labels for 'new_observation'. If specified then will be added to ceteris paribus plots.
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.
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'.
grid_points	number of points for profile. Will be passed to 'calculate_variable_splits()'.
data	validation dataset, will be extracted from ‘x' if it’s an explainer
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 'individual_variable_profile_explainer'. A data frame with calculated LCE profiles.

Examples

library("DALEX2")
 ## 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 = apartments[,2:6], y = apartments$m2.price)

new_apartment <- apartments[1, ]

lce_rf <- individual_conditional_expectations(explainer_rf, new_apartment)
lce_rf

lce_rf <- individual_conditional_expectations(explainer_rf, new_apartment,
          y = new_apartment$m2.price)
lce_rf

plot(lce_rf)

## End(Not run)

pbiecek/ceterisParibus2 documentation built on Sept. 16, 2019, 6:26 p.m.