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LARS Algorithm.pdf

lars.pdf
Preview of LARS Algorithm
🔗 Source: mlpack.org
📊 Size: 342 KB
📄 Pages: 44 pages
⬇️ Downloads: 86

Summary

Least Angle Regression (LARS) is a new model selection algorithm and a less greedy version of traditional forward selection methods. It has three main properties: (1) it can implement the Lasso, which constrains the sum of absolute regression coefficients, and calculate all possible Lasso estimates efficiently; (2) it can implement Forward Stagewise linear regression, explaining the similar numerical results between Lasso and Stagewise; and (3) it provides a simple approximation for the degrees of freedom of a LARS estimate, allowing for a principled choice among possible estimates. LARS and its variants are computationally efficient, requiring only the same order of magnitude of computational effort as Ordinary Least Squares.

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Description

Least Angle Regression (LARS) is a new model selection algorithm and a less greedy version of traditional forward selection methods.

Technical Information

  • File Format: PDF
  • File Size: 342 KB
  • Pages: 44
  • Language: EN
  • Total Downloads: 86
  • Last Updated: 1 week ago

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This PDF document about LARS Algorithm provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into LARS Algorithm.

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