prepare_data: Generate data used for predictions

Description

Generate data used for predictions

Usage

prepare_data(x, ...)
# S3 method for empirical
prepare_data(x, seed = 1, n_samples = 1000, index_features = NULL, ...)
# S3 method for gaussian
prepare_data(x, seed = 1, n_samples = 1000, index_features = NULL, ...)
# S3 method for copula
prepare_data(
  x,
  x_test_gaussian = 1,
  seed = 1,
  n_samples = 1000,
  index_features = NULL,
  ...
)
# S3 method for ctree
prepare_data(
  x,
  seed = 1,
  n_samples = 1000,
  index_features = NULL,
  mc_cores = 1,
  mc_cores_create_ctree = mc_cores,
  mc_cores_sample_ctree = mc_cores,
  ...
)

Arguments

x: Explainer object. See explain for more information.
...: Currently not used.
seed: Positive integer. If NULL the seed will be inherited from the calling environment.
n_samples: Integer. The number of obs to sample from the leaf if sample = TRUE or if sample = FALSE but n_samples is less than the number of obs in the leaf.
index_features: List. Default is NULL but if either various methods are being used or various mincriterion are used for different numbers of conditioned features, this will be a list with the features to pass.
x_test_gaussian: Matrix. Test data quantile-transformed to standard Gaussian variables. Only applicable if approach = "empirical".
mc_cores: Integer. Only for class ctree currently. The number of cores to use in paralellization of the tree building (create_ctree) and tree sampling (sample_ctree). Defaults to 1. Note: Uses parallel::mclapply which relies on forking, i.e. uses only 1 core on Windows systems.
mc_cores_create_ctree: Integer. Same as mc_cores, but specific for the tree building function #' Defaults to mc_cores.
mc_cores_sample_ctree: Integer. Same as mc_cores, but specific for the tree building prediction function. Defaults to mc_cores.