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Experiments for DeepSHAP paper.

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DeepSHAP

Repository for the DeepSHAP experiments.

Prerequisites

  • Python, NumPy, Tensorflow, Keras, XGBoost.

Experiments

Experiments for evaluating baseline distributions are in:

  • 1_multiple_references/

Experiments for evaluating series of models are in:

  • 2_gene_expression_pathway/
  • 3_loss_explanation/
  • 4_feature_extraction/
  • 5_model_stack/

Code

Code underlying the experiments and implementations of DeepSHAP for our specific applications is found in deepshap/.

Dataset availability

The NHANES I, NHANES 1999-2014, CIFAR, and MNIST data sets are all publicly available. The HELOC data set can be obtained by accepting the data set usage license: (https://community.fico.com/s/explainable-machine-learning-challenge?tabset-3158a=a4c37). Metabric data access is restricted and requires getting an approval through Sage Bionetworks Synapse website: https://www.synapse.org/#!Synapse:syn1688369 and https://www.synapse.org/#!Synapse:syn1688370. ROSMAP data access is restricted and requires getting an approval through Sage Bionetworks Synapse website: https://www.synapse.org/#!Synapse:syn3219045 and is available as part of the AD Knowledge Portal https://adknowledgeportal.synapse.org/.

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Experiments for DeepSHAP paper.

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  • Python 1.9%