LASSO-LARS IC System

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

• AKA: LASSO-LARS Information Criteria System.
• Context:
  • It ranges from being a LASSO Regression System to being a LARS System.
  • It can use a Akaike Information Criterion and a Bayes Information Criterion.
• Example(s):
  • sklearn.linear_model.LassoLarsIC [1].
    • Lasso model selection: Cross-Validation / AIC / BIC
• Counter-Example(s):
  • LASSO Regression System.
  • LASSO-LARS CV System.
  • LASSO CV 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#information-criteria-based-model-selection Retrieved:2017-09-17
  • QUOTE: Alternatively, the estimator LassoLarsIC proposes to use the Akaike information criterion (AIC) and the Bayes Information criterion (BIC). It is a computationally cheaper alternative to find the optimal value of alpha as the regularization path is computed only once instead of k+1 times when using k-fold cross-validation. However, such criteria needs a proper estimation of the degrees of freedom of the solution, are derived for large samples (asymptotic results) and assume the model is correct, i.e. that the data are actually generated by this model. They also tend to break when the problem is badly conditioned (more features than samples).

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