LASSO-LARS System

A LASSO-LARS System is a LASSO Regression System that implements a LARS Algorithm to solve a LASSO-LARS Regression Task.

• AKA: LASSO-LARS Regression System.
• Example(s):
  • sklearn.linear_model.LassoLars [1].
    • Lasso path using LARS
• Counter-Example(s):
  • LARS System.
  • LASSO Regression System.
  • LASSO-LARS CV System.
  • LASSO-LARS IC System.
  • Multi-Task Lasso System.
  • ElasticNet System.
• See: Cross-Validation Task, L1-Norm, Stepwise Regression.

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References

2017

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/linear_model.html#using-cross-validation Retrieved:2017-09-17
  • QUOTE: LassoLars is a lasso model implemented using the LARS algorithm, and unlike the implementation based on coordinate_descent, this yields the exact solution, which is piecewise linear as a function of the norm of its coefficients

    (...)

    The Lars algorithm provides the full path of the coefficients along the regularization parameter almost for free, thus a common operation consist of retrieving the path with function lars_path

    (...)

    The algorithm is similar to forward stepwise regression, but instead of including variables at each step, the estimated parameters are increased in a direction equiangular to each one’s correlations with the residual.

    Instead of giving a vector result, the LARS solution consists of a curve denoting the solution for each value of the L1 norm of the parameter vector. The full coefficients path is stored in the array coef_path_, which has size (n_features, max_features+1). The first column is always zero.

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