Well, it is alright if you do not have even basic level exposure to Game Theory. We will introduce and talk about Shapley Values for machine learning interpretability. We can do this with the application of Game Theory! So in this article, we will further explore an alternative method of interpreting Machine Learning models which stems out of the game theory discipline.
What if we can build an interesting decision theory to support all the key decisions with utmost accuracy? That would be wonderful, right? It could simply solve all the confusion, like – the first batsmen to send for batting, the bowler to be chosen, the field placement, etc. Wait – what does all of this have to do with machine learning interpretability, Shapley values, and Game Theory? Here’s a thought to put things into context: Isn’t that a very stressful job? Especially when the hopes of millions of people dwell upon you? The world will be taken by storm by the Indian Cricket Team at the T-20 World Cup in 2020! Our captain Virat Kohli will once again be in the spotlight for taking critical decisions. Ouch! That’s quite a bit of a reduction in revenue.īut this decision was the most rewarding one in the long run, and now its the highest revenue grossing format in the history of cricket! The world was harping about it but the cricket associations were looking at it with caution – the commercial breaks were reduced from 99 seconds to 39 seconds. Let’s roll back the time to 2007 when the first-ever cricket T20 World Cup was organized. It’s a unique and different perspective to interpret black-box machine learning models.
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Learn how to use Shapley values in game theory for machine learning interpretability.
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