Data Shapley: Equitable Valuation of Data for Machine Learning

Code for implementation of "Data Shapley: Equitable Valuation of Data for Machine Learning".

Please cite the following work if you use this benchmark or the provided tools or implementations:

[1] Ghorbani, Amirata, Abubakar Abid, and James Zou. 
"Interpretation of neural networks is fragile." 
Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.

Prerequisites

• Python, NumPy, Tensorflow, Scikit-learn, Matplotlib

Basic Usage

To divide value fairly between individual train data points/sources given the learning algorithm and a meausre of performance for the trained model (test accuracy, etc)