Fit white box model to the simulated data.

Fit white box model to the simulated data.

fit_explanation2(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 consting 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

Examples

# NOT RUN {
fitted_explanation <- fit_explanation2(local_exploration1, "regr.lm", selection = TRUE)
# }