PRRO combines signal-based data pruning and column reordering to improve the supervised learning utility of synthetic tabular data, but its evaluation is undermined by data manipulation and an ill-defined correlation measure.
Enlarging of the sample to address multicollinearity
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The paper analyzes how the enlarging of the sample affects to the mitigation of collinearity concluding that it may mitigate the consequences of collinearity related to statistical analysis but not necessarily the numerical instability. The problem that is addressed is of importance in the teaching of social sciences since it discusses one of the solutions proposed almost unanimously to solve the problem of multicollinearity. For a better understanding and illustration of the contribution of this paper, two empirical examples are presented and not highly technical developments are used.
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Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering
PRRO combines signal-based data pruning and column reordering to improve the supervised learning utility of synthetic tabular data, but its evaluation is undermined by data manipulation and an ill-defined correlation measure.