create_marginal_data_training: Function that samples data from the empirical marginal...

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

Function that samples data from the empirical marginal training distribution

Description

Sample observations from the empirical distribution P(X) using the training dataset.

Usage

create_marginal_data_training(
  x_train,
  n_explain,
  Sbar_features,
  n_MC_samples = 1000,
  stable_version = TRUE
)

Arguments

x_train	Data.table with training data.
Sbar_features	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.
stable_version	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.

Value

Data table of dimension n_MC_samples \times length(Sbar_features) with the sampled observations.

Author(s)

Lars Henry Berge Olsen

Examples

## Not run: 
data("airquality")
data <- data.table::as.data.table(airquality)
data <- data[complete.cases(data), ]

x_var <- c("Solar.R", "Wind", "Temp", "Month")
y_var <- "Ozone"

ind_x_explain <- 1:6
x_train <- data[-ind_x_explain, ..x_var]
x_train
shapr:::create_marginal_data_training(
  x_train = x_train,
  Sbar_features = c(1, 4),
  n_MC_samples = 10
)

## End(Not run)

NorskRegnesentral/shapr documentation built on Feb. 11, 2025, 6:41 a.m.