Machine learning has proven increasingly essential in many fields but a lot obstacles still hinder its use by non-experts. The lack of trust in the results obtained is foremost among them, and has inspired several explanatory approaches in the literature. These approaches provide a great insight on the predictions of a model, but at a cost of a long computation time. In this paper, we aim to further improve the detection of relevant attributes influencing a prediction, on the strength of feature selection methods.
CITATION STYLE
Ferrettini, G., Aligon, J., & Soulé-Dupuy, C. (2020). Improving on Coalitional Prediction Explanation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12245 LNCS, pp. 122–135). Springer. https://doi.org/10.1007/978-3-030-54832-2_11
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