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penalized-regression-on-high-dimensional-data

We explored various approaches to deal with high-dimensional data in this study, and we compared them using simulation and soil datasets. We discovered that grouping had a significant impact on model correctness and error reduction. For the core projection step, we first looked at the properties of all the algorithms and how they function to come up with the best possible answer and which technique outperforms the others and why. OSCAR is a competitive regularize for classification and regression problems, with the extra capability of automatic feature aggregation, as computed and illustrated in the experiments.