shapley_weights

Positive integer. Total number of features.

Positive integer. The number of unique combinations when sampling <code>n_features</code> features,
without replacement, from a sample space consisting of <code>m</code> different features.

Positive integer. Represents the number of features you want to sample from a feature
space consisting of <code>m</code> unique features. Note that <code> 0 &lt; = n_features &lt;= m</code>.

n_features

Positive integer. Represents the Shapley weight for two special
cases, i.e. the case where you have either <code>0</code> or <code>m</code> features.

weight_zero_m

internal

Complex machine learning models are often hard to interpret. However, in
many situations it is crucial to understand and explain why a model made a specific
prediction. Shapley values is the only method for such prediction explanation framework
with a solid theoretical foundation. Previously known methods for estimating the Shapley
values do, however, assume feature independence. This package implements the method
described in Aas, Jullum and L<c3><b8>land (2019) <arXiv:1903.10464>, which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.

Martin Jullum

shapley_weights: Calculate Shapley weight

Description

Usage

Arguments

Value