# feature_combinations: Define feature combinations, and fetch additional information... In shapr: Prediction Explanation with Dependence-Aware Shapley Values

## Usage

 ```1 2 3 4 5 6``` ```feature_combinations( m, exact = TRUE, n_combinations = 200, weight_zero_m = 10^6 ) ```

## Arguments

 `m` Positive integer. Total number of features. `exact` Logical. If `TRUE` all `2^m` combinations are generated, otherwise a subsample of the combinations is used. `n_combinations` Positive integer. Note that if `exact = TRUE`, `n_combinations` is ignored. However, if `m > 12` you'll need to add a positive integer value for `n_combinations`. `weight_zero_m` Numeric. The value to use as a replacement for infinite combination weights when doing numerical operations.

## Value

A data.table that contains the following columns:

id_combination

Positive integer. Represents a unique key for each combination. Note that the table is sorted by `id_combination`, so that is always equal to `x[["id_combination"]] = 1:nrow(x)`.

features

List. Each item of the list is an integer vector where `features[[i]]` represents the indices of the features included in combination `i`. Note that all the items are sorted such that `features[[i]] == sort(features[[i]])` is always true.

n_features

Vector of positive integers. `n_features[i]` equals the number of features in combination `i`, i.e. `n_features[i] = length(features[[i]])`.

.

N

Positive integer. The number of unique ways to sample `n_features[i]` features from `m` different features, without replacement.

## Author(s)

Nikolai Sellereite, Martin Jullum

## Examples

 ```1 2 3 4 5 6``` ```# All combinations x <- feature_combinations(m = 3) nrow(x) # Equals 2^3 = 8 # Subsample of combinations x <- feature_combinations(exact = FALSE, m = 10, n_combinations = 1e2) ```

shapr documentation built on Jan. 28, 2021, 5:06 p.m.