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3. LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of regression. As penalty increases more coefficients are becomes zero and vice Versa. It uses L1 normalisation technique in which tuning parameter is ...
Furthermore, if you are interest in the absolute sparsest solution with the best prediction performance then L0 penalized regression (aka best subset, i.e. based on penalization of the nr of nonzero coefficients as opposed to the sum of the absolute value of the coefficients in LASSO) is better than LASSO, see e.g. the l0ara package and my own L0glm package (in development, some benchmarks here) which approximates L0 penalized GLMs using an iterative adaptive ridge procedure, and which ...
The features I'm using are mainly Ngrams (every N consecutive words) and I'm using the LASSO specifically so that I can rank the features and extract the set of the significant Ngrams in the classification problem. My question is about tuning the alpha parameter in the scikitlearn model: I understand that as I set alpha closer to 1, the number ...
Nov 18, 2010 · In addition LARS is computationally fast and reliable. Lasso is fast but there is a tiny difference between algorithm that causes the LARS win the speed challenge. On the other hand there are alternative packages for example in R, called 'glmnet' that work more reliable than lars package (because it is more general).
Jul 5, 2018 · Now, when doing lasso regression, it is standard practice to standardize the columns in the design matrix, which essentially makes all the predictors dimensionless (though when the coefficients are reported back to the user, they are usually stated on the original scale). You still cannot compare the magnitudes in any reasonable way.
The reason I stress this answer is that during the running of the LASSO the solver generates a sequence of $\lambda$, so while it may counterintuitive providing a single $\lambda$ value may actually slow the solver down considerably (When you provide an exact parameter the solver resorts to solving a semi definite program which can be slow for reasonably 'simple' cases.)
Apr 24, 2016 · 10. When dealing with categorical variables in LASSO regression, it is usual to use a grouped LASSO that keeps the dummy variables corresponding to a particular categorical variable together (i.e., you cannot exclude only some of the dummy variables from the model). A useful method is the Modified Group LASSO (MGL) described in Choi, Park and ...
The LASSO often includes too many variables when selecting the tuning parameter for prediction (minimum CV error). But, with high probability, the true model is a subset of these variables. This suggests using a secondary stage of estimation. The adaptive LASSO and relaxed LASSO both achieve this and control the bias of the LASSO estimate ...
Mar 15, 2017 · 61 1 1 7. 1. Assumptions for what? Cosistency, asymptotic normality, ...? – Richard Hardy. Mar 15, 2017 at 7:45. Possible duplicate of How to interpret the results when both ridge and lasso separately perform well but produce different coefficients.
Dec 15, 2016 · In short, ridge regression and lasso are regression techniques optimized for prediction, rather than inference. Normal regression gives you unbiased regression coefficients (maximum likelihood estimates "as observed in the data-set"). Ridge and lasso regression allow you to regularize ("shrink") coefficients.
