There is many variants of type of solver in liblinear but I don't understand their differences.Which one I must choose?

Also why data must be scaled? duo to some numerical issues?

`-s type : set type of solver (default 1) for multi-class classification 0 -- L2-regularized logistic regression (primal) 1 -- L2-regularized L2-loss support vector classification (dual) 2 -- L2-regularized L2-loss support vector classification (primal) 3 -- L2-regularized L1-loss support vector classification (dual) 4 -- support vector classification by Crammer and Singer 5 -- L1-regularized L2-loss support vector classification 6 -- L1-regularized logistic regression 7 -- L2-regularized logistic regression (dual)`

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#### Best Answer

Here is an article about L1 and L2 loss function

http://www.chioka.in/differences-between-l1-and-l2-as-loss-function-and-regularization/

L1-norm loss function is also known as least absolute deviations (LAD), least absolute errors

L2-norm loss function is also known as least squares error (LSE).

Also, programm will solve faster if you scale your data properly, but it is not necessary when your data amount is very large.

Here is a guide for liblinear.

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