local_permutation_importance: Local permutation variable importance
In MI2DataLab/live: Local Interpretable (Model-Agnostic) Visual Explanations

Description Usage Arguments Value Examples

This function calculates local variable importance (variable drop-out) by finding top_n observations closest to the explained instance, performing permutation variable importance and using weighted mean square error as loss function with weights equal to 1 - Gower distances of the closest observations to the explainedi instance.

1 2	local_permutation_importance(explained_instance, data, explained_var, model, top_n = nrow(data))

`explained_instance`	Data frame with one observation for which prediction will be explained
`data`	Data from with the same columns as explained_instance
`explained_var`	Character with the names of response variable
`model`	Model to be explained
`top_n`	Number of observation that will be used to calculate local variable importance

list of class "local_permutation_importance" that consists of

`residuals`	Data frame with names of variables in the dataset ("label") and values of drop-out loss ("dropout_loss")
`weighted_local_mse`	Value of weighted MSE for the whole dataset with weights given by 1 - Gower distance from the explained instance
`explained_instance`	Explained instance as a data frame

## Not run: 
local_permutation_importance(wine[5, ], wine, 
                             randomForest(quality~., data = wine),
                             top_n = 1000)

## End(Not run)