calculate_profiles_lce: Calculate Local Conditional Expectation profiles
In pbiecek/ceterisParibus2: Ceteris Paribus Profiles

Description Usage Arguments Details Value Examples

This function Local Conditional Expectation profiles

1 2	calculate_profiles_lce(data, variable_splits, model, dataset, predict_function = predict, ...)

`data`	set of observations. Profile will be calculated for every observation (every row)
`variable_splits`	named list of vectors. Elements of the list are vectors with points in which profiles should be calculated. See an example for more details.
`model`	a model that will be passed to the `predict_function`
`dataset`	a data.frame, usually training data of a model, used for calculation of LCE profiles
`predict_function`	function that takes data and model and returns numeric predictions. Note that the ... arguments will be passed to this function.
`...`	other parameters that will be passed to the `predict_function`

Note that calculate_profiles_lce function is S3 generic. If you want to work on non standard data sources (like H2O ddf, external databases) you should overload it.

a data frame with profiles for selected variables and selected observations

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)
vars <- c("construction.year", "surface", "floor", "no.rooms", "district")
variable_splits <- calculate_variable_splits(apartments, vars)
new_apartment <- apartments[1, ]

profiles <- calculate_profiles_lce(new_apartment, variable_splits,
                               apartments_rf_model, explainer_rf$data)
profiles

## End(Not run)