add_predictions {live} | R Documentation |
Add black box predictions to generated dataset
Description
Add black box predictions to generated dataset
Usage
add_predictions(
to_explain,
black_box_model,
data = NULL,
predict_fun = predict,
hyperparams = list(),
...
)
Arguments
to_explain |
List return by sample_locally function. |
black_box_model |
String with mlr signature of a learner or a model with predict interface. |
data |
Original data frame used to generate new dataset. Need not be provided when a trained model is passed in black_box_model argument. |
predict_fun |
Either a "predict" function that returns a vector of the same type as response or custom function that takes a model as a first argument, and data used to calculate predictions as a second argument and returns a vector of the same type as respone. Will be used only if a model object was provided in the black_box argument. |
hyperparams |
Optional list of (hyper)parameters to be passed to mlr::makeLearner. |
... |
Additional parameters to be passed to predict function. |
Value
list of class "live_explorer" consisting of
data |
Dataset generated by sample_locally function with response variable. |
target |
Name of the response variable. |
model |
Black box model which is being explained. |
explained_instance |
Instance that is being explained. |
sampling_method |
Name of used sampling method |
fixed_variables |
Names of variables which were not sampled |
sdevations |
Standard deviations of numerical variables |
Examples
## Not run:
# Train a model inside add_predictions call.
local_exploration1 <- add_predictions(dataset_for_local_exploration,
black_box_model = "regr.svm",
data = wine)
# Pass trained model to the function.
svm_model <- svm(quality ~., data = wine)
local_exploration2 <- add_predictions(dataset_for_local_exploration,
black_box_model = svm_model)
## End(Not run)