Liblinear types of solver 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)


 A: 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.
A: It depends on what you want to do, the L1 and L2 loss are different ways of measuring the loss between the targeted value (label) and the predicted value, with L1 loss being called as least absolute errors and L2 loss being called as least square errors. The regularization is to prevent overfitting. Adding a regularization term can prevent the coefficients to fit so perfectly thus to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. 
The data scaling is neccessary becuase you want to make sure one variable in your model doesn't overcontribute the importance to the result. E.g variable1 ranges from [2000-5000], variable2 ranges from [0.2-0.5], to predict the value between [0,1] without feature scaling, variable1 will contirbute more than variable 2 given the nature of the range. 
