Misc,
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
(2019)cite arxiv:1903.10464.
Abstract
Explaining complex or seemingly simple machine learning models is a practical
and ethical question, as well as a legal issue. Can I trust the model? Is it
biased? Can I explain it to others? We want to explain individual predictions
from a complex machine learning model by learning simple, interpretable
explanations. Of existing work on interpreting complex models, Shapley values
is regarded to be the only model-agnostic explanation method with a solid
theoretical foundation. Kernel SHAP is a computationally efficient
approximation to Shapley values in higher dimensions. Like several other
existing methods, this approach assumes independent features, which may give
very wrong explanations. This is the case even if a simple linear model is used
for predictions. We extend the Kernel SHAP method to handle dependent features.
We provide several examples of linear and non-linear models with linear and
non-linear feature dependence, where our method gives more accurate
approximations to the true Shapley values. We also propose a method for
aggregating individual Shapley values, such that the prediction can be
explained by groups of dependent variables.
