Powershap Algorithm

Powershap is a family of feature selection algorithms leveraging Shapley values, a game-theoretic approach to fairly distribute the contribution of individual features to a machine learning model's prediction. Current research focuses on improving the efficiency and stability of Shapley value estimation, particularly addressing the computational cost associated with high-dimensional datasets, and developing variations like LLpowershap that minimize noise in feature selection. These methods aim to enhance the speed and accuracy of feature selection, leading to more robust and efficient machine learning models across various applications while maintaining predictive performance comparable to more computationally expensive alternatives.