{"id":1468,"date":"2020-01-20T02:53:06","date_gmt":"2020-01-20T02:53:06","guid":{"rendered":"https:\/\/www.danielparente.net\/en\/2020\/01\/20\/from-local-explanations-to-global-understanding-with-explainable-ai-for-trees\/"},"modified":"2020-01-20T02:53:06","modified_gmt":"2020-01-20T02:53:06","slug":"from-local-explanations-to-global-understanding-with-explainable-ai-for-trees","status":"publish","type":"post","link":"https:\/\/www.danielparente.net\/en\/2020\/01\/20\/from-local-explanations-to-global-understanding-with-explainable-ai-for-trees\/","title":{"rendered":"From local explanations to global understanding with explainable AI for trees"},"content":{"rendered":"<p> [ad_1]<br \/>\n<br \/><img decoding=\"async\" src=\"https:\/\/media.springernature.com\/full\/springer-static\/image\/art%3A10.1038%2Fs42256-019-0138-9\/MediaObjects\/42256_2019_138_Fig1_HTML.png\" \/><\/p>\n<div id=\"\">\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"The state of data science &amp; maching learning. Kaggle https:\/\/www.kaggle.com\/surveys\/2017 (2017).\"\/><span class=\"c-article-references__counter\">1.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR1\">The state of data science &amp; maching learning. <i>Kaggle<\/i> <a href=\"https:\/\/www.kaggle.com\/surveys\/2017\" target=\"_blank\" rel=\"noopener\">https:\/\/www.kaggle.com\/surveys\/2017<\/a> (2017).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Friedman, J., Hastie, T. &amp; Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer Series in Stat\"\/><span class=\"c-article-references__counter\">2.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR2\">Friedman, J., Hastie, T. &amp; Tibshirani, R. <i>The Elements of Statistical Learning<\/i> Vol. 1 (Springer Series in Statistics, Springer, 2001).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Lundberg, S. M. &amp; Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. S\"\/><span class=\"c-article-references__counter\">3.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR3\">Lundberg, S. M. &amp; Lee, S.-I. A unified approach to interpreting model predictions. <i>Adv. Neural Inf. Process. Syst.<\/i> <b>30<\/b>, 4768\u20134777 (2017).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Saabas, A. treeinterpreter python package. GitHub https:\/\/github.com\/andosa\/treeinterpreter (2019).\"\/><span class=\"c-article-references__counter\">4.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR4\">Saabas, A. treeinterpreter python package. <i>GitHub<\/i> <a href=\"https:\/\/github.com\/andosa\/treeinterpreter\" target=\"_blank\" rel=\"noopener\">https:\/\/github.com\/andosa\/treeinterpreter<\/a> (2019).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Ribeiro, M. T., Singh, S. &amp; Guestrin, C. Why should i trust you?: Explaining the predictions of any classifier\"\/><span class=\"c-article-references__counter\">5.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR5\">Ribeiro, M. T., Singh, S. &amp; Guestrin, C. Why should i trust you?: Explaining the predictions of any classifier. In <i>Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining<\/i> 1135\u20131144 (ACM, 2016).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Datta, A., Sen, S. &amp; Zick, Y. Algorithmic transparency via quantitative input influence: theory and experiment\"\/><span class=\"c-article-references__counter\">6.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR6\">Datta, A., Sen, S. &amp; Zick, Y. Algorithmic transparency via quantitative input influence: theory and experiments with learning systems. In <i>Proc. 2016 IEEE Symposium on Security and Privacy (SP)<\/i>, 598\u2013617 (IEEE, 2016).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"E. &#x160;trumbelj, I. Kononenko, \"\/><meta itemprop=\"datePublished\" content=\"2014\"\/><meta itemprop=\"headline\" content=\"&#x160;trumbelj, E. &amp; Kononenko, I. Explaining prediction models and individual predictions with feature contributio\"\/><span class=\"c-article-references__counter\">7.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR7\">\u0160trumbelj, E. &amp; Kononenko, I. Explaining prediction models and individual predictions with feature contributions. <i>Knowl. Inf. Syst.<\/i> <b>41<\/b>, 647\u2013665 (2014).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"D. Baehrens, \"\/><meta itemprop=\"datePublished\" content=\"2010\"\/><meta itemprop=\"headline\" content=\"Baehrens, D. et al. How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803&#x2013;1831 (20\"\/><span class=\"c-article-references__counter\">8.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR8\">Baehrens, D. et al. How to explain individual classification decisions. <i>J. Mach. Learn. Res.<\/i> <b>11<\/b>, 1803\u20131831 (2010).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"LS. Shapley, \"\/><meta itemprop=\"datePublished\" content=\"1953\"\/><meta itemprop=\"headline\" content=\"Shapley, L. S. A value for n-person games. Contrib. Theor. Games 2, 307&#x2013;317 (1953).\"\/><span class=\"c-article-references__counter\">9.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR9\">Shapley, L. S. A value for <i>n<\/i>-person games. <i>Contrib. Theor. Games<\/i> <b>2<\/b>, 307\u2013317 (1953).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Sundararajan, M. &amp; Najmi, A. The many Shapley values for model explanation. Preprint at https:\/\/arxiv.org\/abs\/\"\/><span class=\"c-article-references__counter\">10.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR10\">Sundararajan, M. &amp; Najmi, A. The many Shapley values for model explanation. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1908.08474\" target=\"_blank\" rel=\"noopener\">https:\/\/arxiv.org\/abs\/1908.08474<\/a> (2019).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Janzing, D., Minorics, L. &amp; Bl&#xF6;baum, P. Feature relevance quantification in explainable AI: a causality proble\"\/><span class=\"c-article-references__counter\">11.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR11\">Janzing, D., Minorics, L. &amp; Bl\u00f6baum, P. Feature relevance quantification in explainable AI: a causality problem. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1910.13413\" target=\"_blank\" rel=\"noopener\">https:\/\/arxiv.org\/abs\/1910.13413<\/a> (2019).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"Y. Matsui, T. Matsui, \"\/><meta itemprop=\"datePublished\" content=\"2001\"\/><meta itemprop=\"headline\" content=\"Matsui, Y. &amp; Matsui, T. NP-completeness for calculating power indices of weighted majority games. Theor. Compu\"\/><span class=\"c-article-references__counter\">12.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR12\">Matsui, Y. &amp; Matsui, T. NP-completeness for calculating power indices of weighted majority games. <i>Theor. Comput. Sci.<\/i> <b>263<\/b>, 305\u2013310 (2001).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"K. Fujimoto, I. Kojadinovic, J-L. Marichal, \"\/><meta itemprop=\"datePublished\" content=\"2006\"\/><meta itemprop=\"headline\" content=\"Fujimoto, K., Kojadinovic, I. &amp; Marichal, J.-L. Axiomatic characterizations of probabilistic and cardinal-prob\"\/><span class=\"c-article-references__counter\">13.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR13\">Fujimoto, K., Kojadinovic, I. &amp; Marichal, J.-L. Axiomatic characterizations of probabilistic and cardinal-probabilistic interaction indices. <i>Games Econ. Behav.<\/i> <b>55<\/b>, 72\u201399 (2006).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Ribeiro, M. T., Singh, S. &amp; Guestrin, C. Anchors: high-precision model-agnostic explanations. In Proc. AAAI Co\"\/><span class=\"c-article-references__counter\">14.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR14\">Ribeiro, M. T., Singh, S. &amp; Guestrin, C. Anchors: high-precision model-agnostic explanations. In <i>Proc. AAAI Conference on Artificial Intelligence<\/i> (2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"EH. Shortliffe, MJ. Sep&#xFA;lveda, \"\/><meta itemprop=\"datePublished\" content=\"2018\"\/><meta itemprop=\"headline\" content=\"Shortliffe, E. H. &amp; Sep&#xFA;lveda, M. J. Clinical decision support in the era of artificial intelligence. JAMA 320\"\/><span class=\"c-article-references__counter\">15.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR15\">Shortliffe, E. H. &amp; Sep\u00falveda, M. J. Clinical decision support in the era of artificial intelligence. <i>JAMA<\/i> <b>320<\/b>, 2199\u20132200 (2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"SM. Lundberg, \"\/><meta itemprop=\"datePublished\" content=\"2018\"\/><meta itemprop=\"headline\" content=\"Lundberg, S. M. et al. Explainable machine learning predictions to help anesthesiologists prevent hypoxemia du\"\/><span class=\"c-article-references__counter\">16.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR16\">Lundberg, S. M. et al. Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery. <i>Nat. Biomed. Eng.<\/i> <b>2<\/b>, 749\u2013760 (2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"CS. Cox, \"\/><meta itemprop=\"datePublished\" content=\"1997\"\/><meta itemprop=\"headline\" content=\"Cox, C. S. et al. Plan and operation of the NHANES I Epidemiologic Followup Study, 1992. Vital Health Stat. 35\"\/><span class=\"c-article-references__counter\">17.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR17\">Cox, C. S. et al. Plan and operation of the NHANES I Epidemiologic Followup Study, 1992. <i>Vital Health Stat.<\/i> <b>35<\/b>, 1\u2013231 (1997).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Chen, T. &amp; Guestrin, C. Xgboost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Confe\"\/><span class=\"c-article-references__counter\">18.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR18\">Chen, T. &amp; Guestrin, C. Xgboost: a scalable tree boosting system. In <i>Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining<\/i> 785\u2013794 (ACM, 2016).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"S. Haufe, \"\/><meta itemprop=\"datePublished\" content=\"2014\"\/><meta itemprop=\"headline\" content=\"Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroi\"\/><span class=\"c-article-references__counter\">19.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR19\">Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. <i>Neuroimage<\/i> <b>87<\/b>, 96\u2013110 (2014).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Kim, B. et al. Interpretability beyond feature attribution: quantitative testing with concept activation vecto\"\/><span class=\"c-article-references__counter\">20.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR20\">Kim, B. et al. Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In <i>International Conference on Machine Learning<\/i> (ICLR, 2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. &amp; Lipson, H. Understanding neural networks through deep visuali\"\/><span class=\"c-article-references__counter\">21.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR21\">Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. &amp; Lipson, H. Understanding neural networks through deep visualization. In <i>ICML Deep Learning Workshop<\/i> (ICML, 2015).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Bau, D., Zhou, B., Khosla, A., Oliva, A. &amp; Torralba, A. Network dissection: quantifying interpretability of de\"\/><span class=\"c-article-references__counter\">22.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR22\">Bau, D., Zhou, B., Khosla, A., Oliva, A. &amp; Torralba, A. Network dissection: quantifying interpretability of deep visual representations. In <i>Proc. IEEE Conference on Computer Vision and Pattern Recognition<\/i> 6541\u20136549 (IEEE, 2017).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Leino, K., Sen, S., Datta, A., Fredrikson, M. &amp; Li, L. Influence-directed explanations for deep convolutional \"\/><span class=\"c-article-references__counter\">23.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR23\">Leino, K., Sen, S., Datta, A., Fredrikson, M. &amp; Li, L. Influence-directed explanations for deep convolutional networks. In <i>Proc. 2018 IEEE International Test Conference (ITC)<\/i> 1\u20138 (IEEE, 2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"SR. Group, \"\/><meta itemprop=\"datePublished\" content=\"2015\"\/><meta itemprop=\"headline\" content=\"Group, S. R. A randomized trial of intensive versus standard blood-pressure control. N. Engl. J. Med. 373, 210\"\/><span class=\"c-article-references__counter\">24.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR24\">Group, S. R. A randomized trial of intensive versus standard blood-pressure control. <i>N. Engl. J. Med.<\/i> <b>373<\/b>, 2103\u20132116 (2015).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"D. Mozaffarian, \"\/><meta itemprop=\"datePublished\" content=\"2016\"\/><meta itemprop=\"headline\" content=\"Mozaffarian, D. et al. Heart disease and stroke statistics-2016 update a report from the American Heart Associ\"\/><span class=\"c-article-references__counter\">25.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR25\">Mozaffarian, D. et al. Heart disease and stroke statistics-2016 update a report from the American Heart Association. <i>Circulation<\/i> <b>133<\/b>, e38\u2013e48 (2016).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"B. Bowe, Y. Xie, H. Xian, T. Li, Z. Al-Aly, \"\/><meta itemprop=\"datePublished\" content=\"2017\"\/><meta itemprop=\"headline\" content=\"Bowe, B., Xie, Y., Xian, H., Li, T. &amp; Al-Aly, Z. Association between monocyte count and risk of incident CKD a\"\/><span class=\"c-article-references__counter\">26.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR26\">Bowe, B., Xie, Y., Xian, H., Li, T. &amp; Al-Aly, Z. Association between monocyte count and risk of incident CKD and progression to ESRD. <i>Clin. J. Am. Soc. Nephrol.<\/i> <b>12<\/b>, 603\u2013613 (2017).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"F. Fan, J. Jia, J. Li, Y. Huo, Y. Zhang, \"\/><meta itemprop=\"datePublished\" content=\"2017\"\/><meta itemprop=\"headline\" content=\"Fan, F., Jia, J., Li, J., Huo, Y. &amp; Zhang, Y. White blood cell count predicts the odds of kidney function decl\"\/><span class=\"c-article-references__counter\">27.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR27\">Fan, F., Jia, J., Li, J., Huo, Y. &amp; Zhang, Y. White blood cell count predicts the odds of kidney function decline in a Chinese community-based population. <i>BMC Nephrol.<\/i> <b>18<\/b>, 190 (2017).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Zinkevich, M. Rules of machine learning: best practices for ML engineering (2017).\"\/><span class=\"c-article-references__counter\">28.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR28\">Zinkevich, M. Rules of machine learning: best practices for ML engineering (2017).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"SM. Rooden, \"\/><meta itemprop=\"datePublished\" content=\"2010\"\/><meta itemprop=\"headline\" content=\"van Rooden, S. M. et al. The identification of Parkinson&#x2019;s disease subtypes using cluster analysis: a systemat\"\/><span class=\"c-article-references__counter\">29.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR29\">van Rooden, S. M. et al. The identification of Parkinson\u2019s disease subtypes using cluster analysis: a systematic review. <i>Mov. Disord.<\/i> <b>25<\/b>, 969\u2013978 (2010).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"T. S&#xF8;rlie, \"\/><meta itemprop=\"datePublished\" content=\"2003\"\/><meta itemprop=\"headline\" content=\"S&#xF8;rlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc\"\/><span class=\"c-article-references__counter\">30.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR30\">S\u00f8rlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. <i>Proc. Natl Acad. Sci. USA<\/i> <b>100<\/b>, 8418\u20138423 (2003).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"S. Lapuschkin, \"\/><meta itemprop=\"datePublished\" content=\"2019\"\/><meta itemprop=\"headline\" content=\"Lapuschkin, S. et al. Unmasking clever hans predictors and assessing what machines really learn. Nat. Commun. \"\/><span class=\"c-article-references__counter\">31.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR31\">Lapuschkin, S. et al. Unmasking clever hans predictors and assessing what machines really learn. <i>Nat. Commun.<\/i> <b>10<\/b>, 1096 (2019).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Pfungst, O. Clever Hans: (the Horse of Mr. Von Osten.) A Contribution to Experimental Animal and Human Psychol\"\/><span class=\"c-article-references__counter\">32.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR32\">Pfungst, O. Clever Hans: (the Horse of Mr. Von Osten.) A Contribution to Experimental Animal and Human Psychology (Holt, Rinehart and Winston, 1911).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Machine Learning Recommendations for Policymakers (IIF, 2019); https:\/\/www.iif.com\/Publications\/ID\/3574\/Machin\"\/><span class=\"c-article-references__counter\">33.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR33\"><i>Machine Learning Recommendations for Policymakers<\/i> (IIF, 2019); <a href=\"https:\/\/www.iif.com\/Publications\/ID\/3574\/Machine-Learning-Recommendations-for-Policymakers\" target=\"_blank\" rel=\"noopener\">https:\/\/www.iif.com\/Publications\/ID\/3574\/Machine-Learning-Recommendations-for-Policymakers<\/a>\n<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Deeks, A. The judicial demand for explainable artificial intelligence. (2019).\"\/><span class=\"c-article-references__counter\">34.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR34\">Deeks, A. The judicial demand for explainable artificial intelligence. (2019).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"G. Plumb, D. Molitor, AS. Talwalkar, \"\/><meta itemprop=\"datePublished\" content=\"2018\"\/><meta itemprop=\"headline\" content=\"Plumb, G., Molitor, D. &amp; Talwalkar, A. S. Model agnostic supervised local explanations. Adv. Neural Inf. Proce\"\/><span class=\"c-article-references__counter\">35.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR35\">Plumb, G., Molitor, D. &amp; Talwalkar, A. S. Model agnostic supervised local explanations. <i>Adv. Neural Inf. Process. Syst.<\/i> <b>31<\/b>, 2520\u20132529 (2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"HP. Young, \"\/><meta itemprop=\"datePublished\" content=\"1985\"\/><meta itemprop=\"headline\" content=\"Young, H. P. Monotonic solutions of cooperative games. Int. J. Game Theor. 14, 65&#x2013;72 (1985).\"\/><span class=\"c-article-references__counter\">36.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR36\">Young, H. P. Monotonic solutions of cooperative games. <i>Int. J. Game Theor.<\/i> <b>14<\/b>, 65\u201372 (1985).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Ancona, M., Ceolini, E., Oztireli, C. &amp; Gross, M. Towards better understanding of gradient-based attribution m\"\/><span class=\"c-article-references__counter\">37.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR37\">Ancona, M., Ceolini, E., Oztireli, C. &amp; Gross, M. Towards better understanding of gradient-based attribution methods for deep neural networks. In <i>Proc. 6th International Conference on Learning Representations (ICLR 2018)<\/i> (2018).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Hooker, S., Erhan, D., Kindermans, P.-J. &amp; Kim, B. A benchmark for interpretability methods in deep neural net\"\/><span class=\"c-article-references__counter\">38.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR38\">Hooker, S., Erhan, D., Kindermans, P.-J. &amp; Kim, B. A benchmark for interpretability methods in deep neural networks. In <i>Conference on<\/i> <i>Neural Information Processing Systems<\/i> (NIPS, 2019).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"headline\" content=\"Shrikumar, A., Greenside, P., Shcherbina, A. &amp; Kundaje, A. Not just a black box: learning important features t\"\/><span class=\"c-article-references__counter\">39.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR39\">Shrikumar, A., Greenside, P., Shcherbina, A. &amp; Kundaje, A. Not just a black box: learning important features through propagating activation differences. Preprint at <a href=\"https:\/\/arxiv.org\/abs\/1605.01713\" target=\"_blank\" rel=\"noopener\">https:\/\/arxiv.org\/abs\/1605.01713<\/a> (2016).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"KL. Lunetta, LB. Hayward, J. Segal, P. Eerdewegh, \"\/><meta itemprop=\"datePublished\" content=\"2004\"\/><meta itemprop=\"headline\" content=\"Lunetta, K. L., Hayward, L. B., Segal, J. &amp; Van Eerdewegh, P. Screening large-scale association study data: ex\"\/><span class=\"c-article-references__counter\">40.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR40\">Lunetta, K. L., Hayward, L. B., Segal, J. &amp; Van Eerdewegh, P. Screening large-scale association study data: exploiting interactions using random forests. <i>BMC Genet.<\/i> <b>5<\/b>, 32 (2004).<\/p>\n<\/li>\n<li class=\"c-article-references__item js-c-reading-companion-references-item\" itemprop=\"citation\" itemscope=\"itemscope\" itemtype=\"http:\/\/schema.org\/ScholarlyArticle\"><meta itemprop=\"author\" content=\"R. Jiang, W. Tang, X. Wu, W. Fu, \"\/><meta itemprop=\"datePublished\" content=\"2009\"\/><meta itemprop=\"headline\" content=\"Jiang, R., Tang, W., Wu, X. &amp; Fu, W. A random forest approach to the detection of epistatic interactions in ca\"\/><span class=\"c-article-references__counter\">41.<\/span>\n<p class=\"c-article-references__text\" id=\"ref-CR41\">Jiang, R., Tang, W., Wu, X. &amp; Fu, W. A random forest approach to the detection of epistatic interactions in case-control studies. <i>BMC Bioinformatics<\/i> <b>10<\/b>, S65 (2009).<\/p>\n<\/li>\n<\/div>\n<p>[ad_2]<br \/>\n<br \/><a href=\"https:\/\/www.nature.com\/articles\/s42256-019-0138-9?code=4105b7e4-47fa-420a-b5b5-d25fef40b5f5&#038;error=cookies_not_supported\" target=\"_blank\" rel=\"noopener\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] 1. The state of data science &amp; maching learning. Kaggle https:\/\/www.kaggle.com\/surveys\/2017 (2017). 2. Friedman, J., Hastie, T. &amp; Tibshirani, R. The Elements of Statistical Learning Vol. 1 (Springer Series in Statistics, Springer, 2001). 3. Lundberg, S. M. &amp; Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4768\u20134777 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1469,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_post_was_ever_published":false},"categories":[94,92,98],"tags":[],"class_list":["post-1468","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-data-science","category-machine-learning"],"blocksy_meta":[],"jetpack_featured_media_url":"https:\/\/e928cfdc7rs.exactdn.com\/info\/uploads\/sites\/3\/2020\/01\/From-local-explanations-to-global-understanding-with-explainable-AI-for.png?strip=all","jetpack_shortlink":"https:\/\/wp.me\/p2TFCd-nG","jetpack_sharing_enabled":true,"jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/posts\/1468","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/comments?post=1468"}],"version-history":[{"count":0,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/posts\/1468\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/media\/1469"}],"wp:attachment":[{"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/media?parent=1468"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/categories?post=1468"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.danielparente.net\/en\/wp-json\/wp\/v2\/tags?post=1468"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}