  Model interpretability is one of the most intriguing problems in most of the
Machine Learning models, particularly for those that are mathematically
sophisticated. Computing Shapley Values are arguably the best approach so far
to find the importance of each feature in a model, at the row level. In other
words, Shapley values represent the importance of a feature for a particular
row, especially for Classification or Regression problems. One of the biggest
limitations of Shapley vales is that, Shapley value calculations assume all the
features are uncorrelated (independent of each other), this assumption is often
incorrect. To address this problem, we present a unified framework to calculate
Shapley values with correlated features. To be more specific, we do an
adjustment (Matrix formulation) of the features while calculating Independent
Shapley values for the rows. Moreover, we have given a Mathematical proof
against the said adjustments. With these adjustments, Shapley values
(Importance) for the features become independent of the correlations existing
between them. We have also enhanced this adjustment concept for more than
features. As the Shapley values are additive, to calculate combined effect of
two features, we just have to add their individual Shapley values. This is
again not right if one or more of the features (used in the combination) are
correlated with the other features (not in the combination). We have addressed
this problem also by extending the correlation adjustment for one feature to
multiple features in the said combination for which Shapley values are
determined. Our implementation of this method proves that our method is
computationally efficient also, compared to original Shapley method.
