Shapley values come from a part of math called cooperative game theory. They help distribute payoffs among players by looking at their contribution to the whole payoff. In machine learning, Shapley values show the role each feature plays in a model's decision. They help figure out how each feature of a complex model affects its prediction. Lundberg and Lee made the use of Shapley values in machine learning well-known in 2017.
Shapley values matter because:
hapley values have a role in machine learning to clarify individual predictions and overall feature importance. This aids in debugging models selecting features, and building trust in the model.Shapley values find use in NLP models to reveal how specific words or phrases contribute to predictions. This aids tasks like sentiment analysis and text classification where being able to explain is crucial.Shapley values aid general AI projects by shedding light on model behavior. This is crucial in applications that involve critical decisions where it's necessary to understand the reasons behind predictions.
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