fit_explanation: Fit white box model to the simulated data.

In MI2DataLab/live: Local Interpretable (Model-Agnostic) Visual Explanations

Description

Fit white box model to the simulated data.

Usage

fit_explanation(live_object, white_box = "regr.lm",
  kernel = gaussian_kernel, standardize = FALSE, selection = FALSE,
  response_family = "gaussian", predict_type = "response",
  hyperpars = list())

Arguments

live_object	List return by add_predictions function.
white_box	String, learner name recognized by mlr package.
kernel	function which will be used to calculate distance between simulated observations and explained instance.
standardize	If TRUE, numerical variables will be scaled to have mean 0, variance 1 before fitting explanation model.
selection	If TRUE, variable selection based on glmnet implementation of LASSO will be performed.
response_family	family argument to glmnet (and then glm) function. Default value is "gaussian"
predict_type	Argument passed to mlr::makeLearner() argument "predict.type". Defaults to "response".
hyperpars	Optional list of values of hyperparameteres of a model.

Value

List of class "live_explainer" that consists of

data	Dataset used to fit explanation model (may have less column than the original)
model	Fitted explanation model
explained_instance	Instance that is being explained
weights	Weights used in model fitting
selected_variables	Names of selected variables

Examples

## Not run: 
fitted_explanation <- fit_explanation(local_exploration1, "regr.lm", selection = TRUE)

## End(Not run)

MI2DataLab/live documentation built on Aug. 21, 2019, 6:05 p.m.