A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Related tags

Deep Learning classifier machine-learning deep-learning random-forest ensemble ensemble-learning game-theory voting-classifier random-forest-classifier explainable-ai explainable-ml weighted-voting-games shapley owen shapley-value game-theory-toolbox voting-game

@misc{rozemberczki2021shapley,
      title = {{The Shapley Value of Classifiers in Ensemble Games}}, 
      author = {Benedek Rozemberczki and Rik Sarkar},
      year = {2021},
      eprint = {2101.02153},
      archivePrefix = {arXiv},
      primaryClass = {cs.LG}
}

import numpy as np
from shapley import PermutationSampler

W = np.random.uniform(0, 1, (1, 7))
W = W/W.sum()
q = 0.5

solver = PermutationSampler()
solver.solve_game(W, q)
shapley_values = solver.get_solution()

$ pip install shapley

$ python setup.py test

$ cd examples
$ python permutation_sampler_example.py