Will it Python? Machine Learning for Hackers, Chapter 6: Regression models with regularization

UPDATE 1/15/2014: This blog is no longer in service.

This post is now located at: http://slendermeans.org/ml4h-ch6.html

Thanks,
-c.

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3 Responses to Will it Python? Machine Learning for Hackers, Chapter 6: Regression models with regularization

  1. I believe that the alpha parameter for glmnet is not the regularization parameter, but the Elastic Net weight that shifts between the LASSO and ridge penalties.

    • Carl says:

      That’s right. (I think I note that in the notebook.) The loss functions are also apparently specified differently in glmnet and scikit-learn’s lasso, since they give penalty parameters of completely different scales (even though the coefficient estimates given those penalty parameters are the same). I haven’t pinned down the discrepancy yet.

  2. Pingback: Will it Python? Machine Learning for Hackers, Chapter 7: Numerical optimization with deterministic and stochastic methods | Slender Means

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