Fit white box model to the simulated data.

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

dataDataset used to fit explanation model (may have less column than the original)

modelFitted explanation model

explained_instanceInstance that is being explained

weightsWeights used in model fitting

selected_variablesNames of selected variables

## Examples

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