90% of Statisticians Agree: Lasso Outperforms Traditional Selection Methods

In a recent study, 90% of statisticians surveyed indicated a preference for using lasso and relaxed lasso over traditional forward and backward selection methods in statistical modeling. The research, which analyzed various approaches to variable selection, found that lasso techniques not only simplify the model-building process but also enhance predictive accuracy. Specifically, the study showed that lasso methods can reduce model complexity by up to 30% while maintaining or improving prediction performance. These findings suggest a significant shift in statistical practice, advocating for the adoption of more efficient and effective methods like lasso in academic and professional settings.

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