LASSO-LARS CV System

A LASSO-LARS CV System is a LASSO Cross-Validation System that implements a LARS Algorithm to solve a LASSO-LARS CV Task.

• AKA: LASSO-LARS Cross-Validation System.
• Example(s):
  • sklearn.linear_model.LassoLarsCV [1].
    • Lasso model selection: Cross-Validation / AIC / BIC
• Counter-Example(s):
  • LARS System.
  • LASSO Regression System.
  • LASSO-LARS System.
  • LASSO-LARS IC System.
  • Multi-Task Lasso System.
  • ElasticNet System.
• See: Cross-Validation Task, L1-Norm.

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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: scikit-learn exposes objects that set the Lasso alpha parameter by cross-validation: LassoCV and LassoLarsCV. LassoLarsCV</a> is based on the Least Angle Regression algorithm explained below.

    For high-dimensional datasets with many collinear regressors, LassoCV is most often preferable. However, LassoLarsCV has the advantage of exploring more relevant values of alpha parameter, and if the number of samples is very small compared to the number of features, it is often faster than LassoCV.

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