Sample observations from the empirical distribution P(X) using the training dataset.
create_marginal_data_training(
x_train,
n_explain,
Sbar_features,
n_MC_samples = 1000,
stable_version = TRUE
)Data table of dimension n_MC_samples
\(\times\)
length(Sbar_features) with the sampled observations.
Data.table with training data.
Vector of integers containing the features indices to generate marginal observations for.
That is, if Sbar_features is c(1,4), then we sample n_MC_samples observations from \(P(X_1, X_4)\) using the
empirical training observations (with replacements). That is, we sample the first and fourth feature values from
the same training observation, so we do not break the dependence between them.
Logical. If TRUE and n_MC_samples > n_train, then we include each training observation
n_MC_samples %/% n_train times and then sample the remaining n_MC_samples %% n_train samples. Only the latter is
done when n_MC_samples < n_train. This is done separately for each explicand. If FALSE, we randomly sample the
from the observations.
Lars Henry Berge Olsen