References¶
Adebayo, Julius A. “Fairml: Toolbox for diagnosing bias in predictive modeling.” Master’s Thesis, MIT, 2016.
Breiman, Leo. “Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author).” Statistical Science 16, no. 3, 2001.
Craven, Mark W. and Shavlik, Jude W. “Extracting tree structured representations of trained networks.” Advances in Neural Information Processing Systems, 1996.
Goldstein, Alex, Kapelner, Adam, Bleich, Justin, and Pitkin, Emil. “Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.” Journal of Computational and Graphical Statistics, no. 24, 2015.
Groeneveld, R.A. and Meeden, G. (1984), “Measuring Skewness and Kurtosis.” The Statistician, 33, 391-399.
Hall, Patrick, Wen Phan, and SriSatish Ambati. “Ideas for Interpreting Machine Learning.” O’Reilly Ideas. O’Reilly Media, 2017.
Hartigan, J. A. and Mohanty, S. (1992), “The RUNT test for multimodality,” Journal of Classification, 9, 63–70.
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. The Elements of Statistical Learning. Springer, 2008.
Lei, Jing, Max G’Sell, Alessandro Rinaldo, Ryan J. Tibshirani, and Larry Wasserman. “Distribution-Free Predictive Inference for Regression.” Journal of the American Statistical Association (just-accepted), 2017.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “Why Should I Trust You?: Explaining the Predictions of Any Classifier.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
Wilkinson, L. (1999). “Dot plots.” The American Statistician, 53, 276–281.
Wilkinson, L., Anand, A., and Grossman, R. (2005), “Graph-theoretic Scagnostics,” in Proceedings of the IEEE Information Visualization 2005, pp. 157–164.