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  "Description": "Model selection algorithms for regression and\nclassification, where the predictors can be continuous or\ncategorical and the number of regressors may exceed the number\nof observations. The selected model consists of a subset of\nnumerical regressors and partitions of levels of factors.\nSzymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and\nAgnieszka Sołtys, 2023. Improving Group Lasso for\nHigh-Dimensional Categorical Data. In: Computational Science –\nICCS 2023. Lecture Notes in Computer Science, vol 14074, p.\n455-470. Springer, Cham. <doi:10.1007/978-3-031-36021-3_47>.\nAleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka\nProchenka, 2015. Delete or merge regressors for linear model\nselection. Electronic Journal of Statistics 9(2): 1749-1778.\n<doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk,\n2015. Combined l1 and greedy l0 penalized least squares for\nlinear model selection. Journal of Machine Learning Research\n16(29): 961-992.\n<https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.\nPiotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał\nFrej and Jan Mielniczuk, 2022. Improving Lasso for model\nselection and prediction. Scandinavian Journal of Statistics,\n49(2): 831–863. <doi:10.1111/sjos.12546>.",
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