Sophisticated statistical models are believed to often bring improved accuracies and efficiencies. But, due to their non-interpretable nature of outputs, they are not very much used by organizations, institutions and governments. They are hence named Black-Boxes. Model interpretability is desired in practical world problems where decisions can have a huge impact (eg. criminal justice, estimating credit scores, health risks etc). Here novel methods that form the state-of-the-art for addressing this particular problem by trying to give a guide to practitioners for appropriate methods to their problems.

In the recent past two decades, machine learning’s applications across many domains advanced very…

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