Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

Journal of Computational and Graphical Statistics

Volumn 24, Issue 1, 2015, Pages 44-65

  Goldstein, Alex ^a   Kapelner, Adam ^b   Bleich, Justin ^a   Pitkin, Emil ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b CITY UNIVERSITY OF NEW YORK   (United States)

Author keywords

Exploratory data analysis; Graphical method; Model visualization

Indexed keywords

EID: 84926207567     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1080/10618600.2014.907095     Document Type: Article

References (28)

1. Bache, K., Lichman, M. (2013), UCI Machine Learning Repository, available at http://archive.ics.uci.edu/ml.
2. Berk, R., Bleich, J. (2013), Statistical Procedures for Forecasting Criminal Behavior: A Comparative Assessment, Criminology and Public Policy, 12, 513–544.
3. Breiman, L. (2001a), Random Forests, Machine Learning, 5–32.
4. ———— (2001b), Statistical Modeling: The Two Cultures, Statistical Science, 16, 199–231.
5. Breiman, L., Friedman, J. (1985), Estimating Optimal Transformations for Multiple Regression and Correlation, Journal of the American Statistical Association, 80, 580–598.
6. Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E., Swayne, D., Wickham, H. (2009), Statistical Inference for Exploratory Data Analysis and Model Diagnostics, Philosophical Transactions, Series A, Mathematical, Physical, and Engineering Sciences, 367, 4361–83.
7. Chipman, H., George, E., McCulloch, R. (2010), BART: Bayesian Additive Regressive Trees, The Annals of Applied Statistics, 4, 266–298.
8. DeRubeis, R., Cohen, Z., Forand, N., Fournier, J., Gelfand, L., Lorenzo-Luaces, L. (2014), The Personalized Advantage Index: Translating Research on Prediction Into Individual Treatment Recommendations A Demonstration, PloS One, 9, e83875.
9. Elith, J., Leathwick, J., Hastie, T. (2008), A Working Guide to Boosted Regression Trees, The Journal of Animal Ecology, 77, 802–813.
10. Friedman, J. (1984), “A Variable Span Smoother,” Technical Report LCS-TR-5, Stanford University, Lab for Computational Statistics.
11. ———— (2001), Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, 29, 1189–1232.
12. (2002), Stochastic Gradient Boosting, Computational Statistics & Data Analysis, 38, 367–378.
13. Green, D., and Kern, H. (2010), “Modeling Heterogeneous Treatment Effects in Large-Scale Experiments Using Bayesian Additive Regression Trees,” in The Annual Summer Meeting of the Society of Political Methodology.
14. Hastie, T. (2013), GAM: Generalized Additive Models, R package version 1.09.
15. Hastie, T., Tibshirani, R. (1986), Generalized Additive Models, Statistical Science, 1, 297–310.
16. Hastie, T., Tibshirani, R., and Friedman, J. (2009), The Elements of Statistical Learning (2nd ed.), New York: Springer Science.
17. Jakulin, A., Mozina, M., and Demsar, J. (2005), “Nomograms for Visualizing Support Vector Machines,” in KDD, pp. 108–117.
18. Jiang, T., and Owen, A. (2002), “Quasi-Regression for Visualization and Interpretation of Black Box Functions,” Technical Report, Stanford University mimeo.
19. Kapelner, A., Bleich, J. (2014), bartMachine: Machine Learning With Bayesian Additive Regression Trees, ArXiv e-prints.
20. Liaw, A., Wiener, M. (2002), Classification and Regression by Randomforest, R News, 2, 18–22.
21. Plate, T., Bert, J., Grace, J., Band, P. (2000), Visualizing the Function Computed by a Feedforward Neural Network, Neural Computation, 12, 1337–1353.
22. Rao, J., and Potts, W. (1997), “Visualizing Bagged Decision Trees,” in KDD-97 Proceedings, pp. 243–246, available at http://www.aaai.org/Papers/KDD/1997/KDD97-050.pdf.
23. Ridgeway, G. (2013), GBM: Generalized Boosted Regression Models, R package version 2.0-8.
24. Smith, J., and Everhart, J. (1988), “Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus,” in Proceedings of the Symposium on Computer Applications and Medical Care, IEEE Computer Society Press, pp. 261–265.
25. Strumbelj, E., and Kononenko, I. (2011), “A General Method for Visualizing and Explaining Black-Box Regression Models,” in Adaptive and Natural Computing Algorithms, Part II, eds. A. Dobnikar, R. Lotric, and B. Ster, Berlin-Heidelberg: Springer, chap. 3, pp. 21–30.
26. Tzeng, F. (2005), “Opening the Black Box—Data Driven Visualization of Neural Networks,” in Visualization, IEEE, pp. 383–390.
27. Venables, W., and Ripley, B. (2002), Modern Applied Statistics With S (4th ed.), New York: Springer.
28. Wickham, H., Cook, D., Hofmann, H., Buja, A. (2010), Graphical Inference for Infovis, IEEE Transactions on Visualization and Computer Graphics, 16, 973–979.