lrmatrix 0.1.5

Logic Rules Matrix package to support the ExMatrix and VAX methods.

Logic Rules Matrix

Logic Rules Matrix is a package to support the Explainable Matrix - ExMatrix and multiVariate dAta eXplanation - VAX methods. Both ExMatrix and VAX employ a matrix-like visual metaphor for logic rules visualization, where rules are rows, features (variables) are columns, and rules predicates are cells.

The ExMatrix must be used for model (predictive) explanations (model interpretability/explainability), while VAX must be employed for data (descriptive) explanations (phenomenon understanding).

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[1] Popolin Neto, M. (2021). Random Forest interpretability - explaining classification models and multivariate data through logic rules visualizations. Doctoral Thesis, Instituto de Ciências Matemáticas e de Computação, University of São Paulo, São Carlos. doi:10.11606/T.55.2021.tde-03032022-105725.

BibTeX: @phdthesis{PopolinNeto:2021:Thesis, doi = {10.11606/t.55.2021.tde-03032022-105725}, publisher = {Universidade de São Paulo, Agência {USP} de Gestão da Informação Academica ({AGUIA})}, author = {Mário Popolin{ }Neto}, title = {Random Forest interpretability - explaining classification models and multivariate data through logic rules visualizations}}

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[2] M. Popolin Neto and F. V. Paulovich, "Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles," in IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1427-1437, Feb. 2021, doi: 10.1109/TVCG.2020.3030354.

BibTeX: @article{PopolinNeto:2020:ExMatrix, author={Popolin{ }Neto, Mário and Paulovich, Fernando V.}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={Explainable Matrix - Visualization for Global and Local Interpretability of Random Forest Classification Ensembles}, year={2021}, volume={27}, number={2}, pages={1427-1437}, doi={10.1109/TVCG.2020.3030354}}

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[3] M. Popolin Neto and F. V. Paulovich, "Multivariate Data Explanation by Jumping Emerging Patterns Visualization," in IEEE Transactions on Visualization and Computer Graphics, 2022, doi: 10.1109/TVCG.2022.3223529.

BibTeX: @article{PopolinNeto:2022:VAX, author={Popolin{ }Neto, Mário and Paulovich, Fernando V.}, journal={IEEE Transactions on Visualization and Computer Graphics}, title={Multivariate Data Explanation by Jumping Emerging Patterns Visualization}, year={2022}, volume={}, number={}, pages={1-16}, doi={10.1109/TVCG.2022.3223529}}

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