local_approximation: Fit local model around the observation: shortcut for DALEX...
In MI2DataLab/live: Local Interpretable (Model-Agnostic) Visual Explanations

Description Usage Arguments Value Examples

Fit local model around the observation: shortcut for DALEX explainer objects

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local_approximation(explainer, observation, target_variable_name,
  n_new_obs, local_model = "regr.lm", select_variables = F,
  predict_type = "response", kernel_type = gaussian_kernel, ...)

`explainer`	a model to be explained, preprocessed by the DALEX::explain function
`observation`	a new observation for which predictions need to be explained
`target_variable_name`	name of the response variablea as a character
`n_new_obs`	Number of observation in the simulated dataset
`local_model`	Character specyfing mlr learner to be used as a local model
`select_variables`	If TRUE, variable selection will be performed while fitting the local linear model
`predict_type`	Argument passed to mlr::makeLearner() argument "predict.type" while fitting the local model. Defaults to "response"
`kernel_type`	Function which will be used to calculate distances from simulated observation to explained instance
`...`	Arguments to be passed to sample_locally function

object of class live_explainer. More details in fit_explanation function help.

## Not run: 
data('wine')
library(randomForest)
library(DALEX)
rf <- randomForest(quality~., data = wine)
expl <- explain(rf, wine, wine$quality)
live_expl <- local_approximation(expl, wine[5, ], "quality", 500)

## End(Not run)