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Get imputed data
observation_impute_cpp(index_xtrain, index_s, x_train, x_explain, S)Numeric matrix
Positive integer. Represents a sequence of row indices from x_train,
i.e. min(index_xtrain) >= 1 and max(index_xtrain) <= nrow(x_train).
Positive integer. Represents a sequence of row indices from S,
i.e. min(index_s) >= 1 and max(index_s) <= nrow(S).
Matrix. Contains the training data.
Matrix with 1 row. Contains the features of the observation for a single prediction.
arma::mat.
Matrix of dimension (n_coalitions, n_features) containing binary representations of the used coalitions.
S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones.
This is not a problem internally in shapr as the empty and grand coalitions are treated differently.
Nikolai Sellereite
S(i, j) = 1 if and only if feature j is present in feature
combination i, otherwise S(i, j) = 0. I.e. if m = 3, there
are 2^3 = 8 unique ways to combine the features. In this case dim(S) = c(8, 3).
Let's call the features x1, x2, x3 and take a closer look at the combination
represented by s = c(x1, x2). If this combination is represented by the second row,
the following is true: S[2, 1:3] = c(1, 1, 0).
The returned object, X, is a numeric matrix where
dim(X) = c(length(index_xtrain), ncol(x_train)). If feature j is present in
the k-th observation, that is S[index_[k], j] == 1, X[k, j] = x_explain[1, j].
Otherwise X[k, j] = x_train[index_xtrain[k], j].