From local explanations to global understanding with explainable AI for trees



  • 1.

    The state of data science & maching learning. Kaggle https://www.kaggle.com/surveys/2017 (2017).

  • 2.

    Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer Series in Statistics, Springer, 2001).

  • 3.

    Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4768–4777 (2017).

  • 4.

    Saabas, A. treeinterpreter python package. GitHub https://github.com/andosa/treeinterpreter (2019).

  • 5.

    Ribeiro, M. T., Singh, S. & Guestrin, C. Why should i trust you?: Explaining the predictions of any classifier. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (ACM, 2016).

  • 6.

    Datta, A., Sen, S. & Zick, Y. Algorithmic transparency via quantitative input influence: theory and experiments with learning systems. In Proc. 2016 IEEE Symposium on Security and Privacy (SP), 598–617 (IEEE, 2016).

  • 7.

    Štrumbelj, E. & Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647–665 (2014).

  • 8.

    Baehrens, D. et al. How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803–1831 (2010).

  • 9.

    Shapley, L. S. A value for n-person games. Contrib. Theor. Games 2, 307–317 (1953).

  • 10.

    Sundararajan, M. & Najmi, A. The many Shapley values for model explanation. Preprint at https://arxiv.org/abs/1908.08474 (2019).

  • 11.

    Janzing, D., Minorics, L. & Blöbaum, P. Feature relevance quantification in explainable AI: a causality problem. Preprint at https://arxiv.org/abs/1910.13413 (2019).

  • 12.

    Matsui, Y. & Matsui, T. NP-completeness for calculating power indices of weighted majority games. Theor. Comput. Sci. 263, 305–310 (2001).

  • 13.

    Fujimoto, K., Kojadinovic, I. & Marichal, J.-L. Axiomatic characterizations of probabilistic and cardinal-probabilistic interaction indices. Games Econ. Behav. 55, 72–99 (2006).

  • 14.

    Ribeiro, M. T., Singh, S. & Guestrin, C. Anchors: high-precision model-agnostic explanations. In Proc. AAAI Conference on Artificial Intelligence (2018).

  • 15.

    Shortliffe, E. H. & Sepúlveda, M. J. Clinical decision support in the era of artificial intelligence. JAMA 320, 2199–2200 (2018).

  • 16.

    Lundberg, S. M. et al. Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery. Nat. Biomed. Eng. 2, 749–760 (2018).

  • 17.

    Cox, C. S. et al. Plan and operation of the NHANES I Epidemiologic Followup Study, 1992. Vital Health Stat. 35, 1–231 (1997).

  • 18.

    Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).

  • 19.

    Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).

  • 20.

    Kim, B. et al. Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In International Conference on Machine Learning (ICLR, 2018).

  • 21.

    Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. & Lipson, H. Understanding neural networks through deep visualization. In ICML Deep Learning Workshop (ICML, 2015).

  • 22.

    Bau, D., Zhou, B., Khosla, A., Oliva, A. & Torralba, A. Network dissection: quantifying interpretability of deep visual representations. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 6541–6549 (IEEE, 2017).

  • 23.

    Leino, K., Sen, S., Datta, A., Fredrikson, M. & Li, L. Influence-directed explanations for deep convolutional networks. In Proc. 2018 IEEE International Test Conference (ITC) 1–8 (IEEE, 2018).

  • 24.

    Group, S. R. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 2103–2116 (2015).

  • 25.

    Mozaffarian, D. et al. Heart disease and stroke statistics-2016 update a report from the American Heart Association. Circulation 133, e38–e48 (2016).

  • 26.

    Bowe, B., Xie, Y., Xian, H., Li, T. & Al-Aly, Z. Association between monocyte count and risk of incident CKD and progression to ESRD. Clin. J. Am. Soc. Nephrol. 12, 603–613 (2017).

  • 27.

    Fan, F., Jia, J., Li, J., Huo, Y. & Zhang, Y. White blood cell count predicts the odds of kidney function decline in a Chinese community-based population. BMC Nephrol. 18, 190 (2017).

  • 28.

    Zinkevich, M. Rules of machine learning: best practices for ML engineering (2017).

  • 29.

    van Rooden, S. M. et al. The identification of Parkinson’s disease subtypes using cluster analysis: a systematic review. Mov. Disord. 25, 969–978 (2010).

  • 30.

    Sørlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl Acad. Sci. USA 100, 8418–8423 (2003).

  • 31.

    Lapuschkin, S. et al. Unmasking clever hans predictors and assessing what machines really learn. Nat. Commun. 10, 1096 (2019).

  • 32.

    Pfungst, O. Clever Hans: (the Horse of Mr. Von Osten.) A Contribution to Experimental Animal and Human Psychology (Holt, Rinehart and Winston, 1911).

  • 33.

    Machine Learning Recommendations for Policymakers (IIF, 2019); https://www.iif.com/Publications/ID/3574/Machine-Learning-Recommendations-for-Policymakers

  • 34.

    Deeks, A. The judicial demand for explainable artificial intelligence. (2019).

  • 35.

    Plumb, G., Molitor, D. & Talwalkar, A. S. Model agnostic supervised local explanations. Adv. Neural Inf. Process. Syst. 31, 2520–2529 (2018).

  • 36.

    Young, H. P. Monotonic solutions of cooperative games. Int. J. Game Theor. 14, 65–72 (1985).

  • 37.

    Ancona, M., Ceolini, E., Oztireli, C. & Gross, M. Towards better understanding of gradient-based attribution methods for deep neural networks. In Proc. 6th International Conference on Learning Representations (ICLR 2018) (2018).

  • 38.

    Hooker, S., Erhan, D., Kindermans, P.-J. & Kim, B. A benchmark for interpretability methods in deep neural networks. In Conference on Neural Information Processing Systems (NIPS, 2019).

  • 39.

    Shrikumar, A., Greenside, P., Shcherbina, A. & Kundaje, A. Not just a black box: learning important features through propagating activation differences. Preprint at https://arxiv.org/abs/1605.01713 (2016).

  • 40.

    Lunetta, K. L., Hayward, L. B., Segal, J. & Van Eerdewegh, P. Screening large-scale association study data: exploiting interactions using random forests. BMC Genet. 5, 32 (2004).

  • 41.

    Jiang, R., Tang, W., Wu, X. & Fu, W. A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics 10, S65 (2009).



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