April 2017
Beginner to intermediate
420 pages
9h 58m
English
book
Mastering Machine Learning with R - Second Edition
by Cory Lesmeister, Doug Ortiz, Vikram Dhillon, Miroslav Kopecky
Content preview from Mastering Machine Learning with R - Second Edition
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LASSO
LASSO applies the L1-norm instead of the L2-norm as in ridge regression, which is the sum of the absolute value of the feature weights and thus minimizes RSS + λ(sum |Bj|). This shrinkage penalty will indeed force a feature weight to zero. This is a clear advantage over ridge regression, as it may greatly improve the model interpretability.
The mathematics behind the reason that the L1-norm allows the weights/coefficients to become zero, is out of the scope of this book (refer to Tibsharini, 1996 for further details).
If LASSO is so great, then ridge regression must be clearly obsolete. Not so fast! In a situation of high collinearity or high pairwise correlations, LASSO may force a predictive feature to zero and thus you can lose the ...
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