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This chapter uses predicting student certification in online courses hosted on edX to demonstrate how to apply three powerful regularization methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP) in constructing a predictive model. These advanced statistical methods are especially valuable in scenarios where we have a multitude of potential predictors and need to select appropriate variables to establish viable predictive models.

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Comparative Analysis of Regularization Methods for Predicting Student Certification

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Tian Li,
  • Feifei Han,
  • Jiesi Guo,
  • Jinran Wu

摘要

This chapter uses predicting student certification in online courses hosted on edX to demonstrate how to apply three powerful regularization methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP) in constructing a predictive model. These advanced statistical methods are especially valuable in scenarios where we have a multitude of potential predictors and need to select appropriate variables to establish viable predictive models.