Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Friedman, J.H., et al.: Multivariate adaptive regression splines. The annals of statistics 19

1991

Earlier work this paper cites.

Askira-Gelman, I.: Knowledge discovery: comprehensibility of the results. In: Proceedings of the thirty-first Hawaii international conference on system sciences. vol. 5, pp. 247–255. IEEE (1998)

1998

Earlier work this paper cites.

Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Annals of statistics pp. 1189–1232 (2001)

2001

Earlier work this paper cites.

Knowles, J.: Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 10

2006

Earlier work this paper cites.

Rüping, S., et al.: Learning interpretable models. Univ. Dortmund (2006), http://d-nb.info/997491736

2006

Earlier work this paper cites.

Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems 47

2009

Earlier work this paper cites.

Schielzeth, H.: Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution 1

2010

Earlier work this paper cites.

Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., Baesens, B.: An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems 51

2011

Earlier work this paper cites.

Fürnkranz, J., Gamberger, D., Lavrač, N.: Foundations of rule learning. Springer Science & Business Media (2012)

2012

Earlier work this paper cites.

Fanaee-T, H., Gama, J.: Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence pp. 1–15 (2013)

2013

Earlier work this paper cites.

Freitas, A.A.: Comprehensible classification models: a position paper. ACM SIGKDD explorations newsletter 15

2014

Earlier work this paper cites.

Apley, D.: Visualizing the effects of predictor variables in black box supervised learning models. arXiv preprint arXiv:1612.08468 (2016)

Original

2016

Earlier work this paper cites.

Bibal, A., Frénay, B.: Interpretability of machine learning models and representations: an introduction. In: Proceedings on ESANN. pp. 77–82 (2016)

2016

Cited alongside, same era.

Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., Casalicchio, G., Jones, Z.M.: mlr: Machine learning in R. Journal of Machine Learning Research 17

2016

Cited alongside, same era.

Horn, D., Bischl, B.: Multi-objective parameter configuration of machine learning algorithms using model-based optimization. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). pp. 1–8. Ieee (2016)

2016

Cited alongside, same era.

Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 (2016)

Original

2016

Cited alongside, same era.

Fisher, A., Rudin, C., Dominici, F.: All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance. arXiv preprint arXiv:1801.01489 (2018)

Original

2018

Later among the works it cites.

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51

2018

Later among the works it cites.

Hamidieh, K.: A data-driven statistical model for predicting the critical temperature of a superconductor. Computational Materials Science 154

2018

Later among the works it cites.

Molnar, C., Bischl, B., Casalicchio, G.: iml: An R package for interpretable machine learning. JOSS 3

2018

Later among the works it cites.

Philipp, M., Rusch, T., Hornik, K., Strobl, C.: Measuring the stability of results from supervised statistical learning. Journal of Computational and Graphical Statistics 27